棉花加工过程中籽棉预处理关键技术研究
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摘要
棉花从采摘下来的籽棉到加工成皮棉产品大体需要经历3个加工过程,分别是准备、加工和打包。其中,准备过程是指烘干并清理籽棉,也被称为籽棉预处理过程。这一过程主要有两个任务。第一是调整籽棉回潮率,籽棉最适合加工的回潮率是6.5～8.5%,回潮率过低会使棉纤维刚性降低,清理时容易将棉纤维拉断,回潮率过高会增大棉纤维与杂质之间的摩擦力,使得杂质不容易被清理。第二是清除杂质,赴轧籽棉应含有尽可能少的杂质,所以在籽棉预处理过程中应尽量多的清理籽棉中的杂质,但过度的清理也会导致棉纤维损伤,从而降低最终棉花产品的质量。
我国棉花种植密度很大,每亩可达到16000～18000株。过密的种植方式给籽棉清理工作带来很多难题。目前棉花采摘大多使用机械采摘方式,由于种植过密,很多棉枝、棉叶、铃壳无法脱落,跟随籽棉一起被采摘。因此棉花加工企业收购的籽棉含杂可高达到20%以上,这就加重了籽棉清理工作的负担。
目前我国棉花加工企业的籽棉预处理加工较为落后,主要体现在检测方式落后、加工设备智能化程度低等方面。当前棉花加工企业主要的加工方式是在加工前根据对籽棉的抽检结果调整籽棉预处理设备的关键参数,在加工过程中这些参数不再进行调整。这是一种粗犷的加工方式,设备运行参数无法根据籽棉性状进行实时调整,预处理后的籽棉回潮率一致性差、杂质含量高、棉纤维损伤大,严重影响了最终皮棉产品的质量。尤其是在加工的后期,新疆12月份的气候环境非常恶劣,严重影响了籽棉回潮率等性状,若按前期的加工方案和设备参数加工,则籽棉预处理后的含杂量和回潮率根本达不到轧花加工的要求。
针对这些问题,本文对籽棉预处理过程中的籽棉含杂量在线检测、籽棉烘干工艺、籽棉清理工艺等关键问题进行了研究,并设计了籽棉预处理智能控制系统。进行的主要工作如下：
(1)根据干燥理论,对籽棉烘干机理进行了研究分析。设计了籽棉热风烘干试验和微波烘干试验,通过试验结果对比分析,提出基于微波-热风联合烘干方式。通过设计的微波-热风联合烘干正交旋转试验建立了籽棉烘干多目标优化控制模型,改进后的烘干工艺有效克服了热风烘干延迟大、能耗大、污染大等缺陷,实现籽棉烘干的多目标优化控制。
(2)构建了一种籽棉含杂量在线检测装置,该装置主要由CCD工业相机、镜头、光源和采样机构组成。通过CCD工业相机对籽棉进行图像采样,为保证采样的准确性,该装置的光源必须能提供充足、均匀的光线。采样的图片经过以太网传递给上位机进行图像处理分析。本文在对大量籽棉和杂质采样照片的分析基础上提出了一种基于纹理的籽棉含杂量在线检测算法,该算法不仅可以精确计算出籽棉的含杂总量,还可以识别杂质的种类,以及每种杂质的含量,为实现籽棉清理智能控制奠定了检测基础。
(3)对籽棉清理机理及现有清理设备进行了分析,发现不同清理设备主要清理的杂质种类不同,同一清理设备在不同的清理工艺环节中清理效率不同。根据这一特点将所有籽棉清理设备按照工艺环节排序,并将其看成一个整体。分析提取了对棉纤维产生损伤和影响籽棉清理效率的因素,其中最主要的有：一级倾斜式籽棉清理机刺钉滚筒转速、提净式籽棉清理机锯齿滚筒转速、二级倾斜式籽棉清理机刺钉滚筒转速和回收式籽棉清理机刺钉滚筒转速,以这些因素为条件设计了正交旋转试验,通过试验结果建立了籽棉清理多目标优化模型,实现籽棉清理的多目标优化控制。
(4)根据本文建立的籽棉烘干和清理多目标优化模型以及籽棉预处理设备的特点设计了籽棉预处理智能控制系统。该系统的硬件结构采用管理层、监控层和现场层3层结构,各层之间采用工业以太网和现场总线相结合的组网技术。该系统软件的控制策略是在建立的籽棉烘干和清理多目标优化模型的基础上提出的籽棉预处理智能控制策略,该智能控制策略以混沌粒子群优化算法为基础,结合籽棉预处理过程的特点,将粒子群进行分组初始化,并对启动混沌机制的判断进行了改进,经过实际生产检验,该智能控制策略能对籽棉预处理过程进行有效控制,优化了预处理过程中各设备运行参数,使籽棉的回潮率一致性更好、含杂量更低、棉纤维损伤更小。
本文的研究为籽棉预处理加工提供了重要的理论和技术支持,为实现棉花精细化加工提供了解决方案,对提高我国棉花加工业自动化和智能化水平有较大的理论意义和实际应用价值。
From being picked to be processed into finished products, cotton generally experienced3processes. They are preparing, processing and packaging. Among them, the preparation process included seed cotton drying and cleaning, also known as the seed cotton pretreatment process. Seed cotton pretreatment process had two main tasks. The first was to adjust the seed cotton moisture regain. Seed cotton moisture regain most suitable for processing was6.5-8.5%. Low moisture regains made cotton fiber stiffness reduction. When being cleaned, the cotton fiber was easy to be broken. Excessive moisture regain would increase the friction between cotton fiber and impurities. So the impurities were not easy to be cleaned. The second was to clean impurities. Seed cotton should contain impurities as little as possible before being ginned. So the cotton processing enterprise should clean the impurities as much as possible in the seed cotton pretreatment process. But excessive cleaning would cause damage to cotton fibers. This could reduce the final quality of the cotton products
China's cotton planting density was large, which could reach16000～8000strain per mu. Many difficulties had product during the seed cleaning process because of the high planting density. At present most cotton was picked by harvester. Because of the high planting density, a lot of cotton plant, cotton leaf and boll shell could not take off. They were picked with seed cotton together. Therefore, the cotton processing enterprise purchased the cotton which had high impurities content. The maximum impurities content could reach more than20%. This problem aggravated the burden of seed cotton cleaning work.
Now, the seed cotton pretreatment process which was applied in cotton processing enterprises was relatively backward in our country. The problems mainly reflected in the detection way backward and low level of automation processing equipment. At present cotton processing enterprises mainly process was that adjust the key parameters of cotton seed processing equipment according to the sampling results before processing. These parameters were not adjusted in the process. This was a rough processing way. The key parameters of cotton seed processing equipment could not be real-time adjusted according to the seed cotton properties. Seed cotton which was pretreatment had a lot of problems. These problems included cotton moisture regain consistency, high impurity content and cotton fibers damage. All of these problems seriously affected the quality of the final cotton product. Especially in the late processing, the climatic environmental of Xinjiang is very poor in December. This serious influenced the properties of seed cotton. According to the early processing scheme and equipment parameters, the impurities content and moisture regain of seed cotton which were processed were not up to the requirements of ginning processing.
Aiming at these problems, this dissertation carried out research into on-line detection of seed cotton impurities content, seed cotton drying process, seed cotton cleaning process and designed the intelligent control system of seed cotton pretreatment. The main research contents of the dissertation area as follows:
(1)According to the drying theory, the seed cotton drying mechanism was analyzed. The seed cotton hot-air drying experiment and microwave drying experiment were designed. The main factors affecting the efficiency of drying included temperature of hot air and microwave power density. The control objectives included the drying efficiency, the damage of fiber specific breaking strength, the damage of Rd and the increment of +b. By analyzing the results of experiment, a new way based on microwave-hot air drying was proposed. The improved drying process effectively overcame the defects of hot-air drying, such as high energy consumption, big delay and pollution etc. By the orthogonal rotation experiment, multi-objective optimization control model of seed cotton drying was established. The seed cotton drying multi-objective optimal control was realized.
(2)A seed cotton impurities content on-line detection device was constructed. The device mainly consisted of CCD industrial camera, lens, the light source and the sampling mechanism. This device used the Pilot series area of black and white CCD industrial camera manufactured by Basler, type PiA2400-17gm, the CCTV series lenses manufactured by Myutron, type HF0528J. The focal length of lens was5mm, could be tuned to5.5mm. The light source used LED light, which color was white. The device sampled through the CCD industrial camera. In order to ensure the accuracy of the sampling, the device must be able to provide adequate and uniform light. Sampling images were transmitted to the host computer through Ethernet. Then the host computer analyzed these sampling images. By the analysis of seed cotton and impurities sampling images, the dissertation presented a cotton impurity content online detection algorithm based on texture. This algorithm not only could accurately calculate the impurity content of seed cotton, but also could identify the types of impurities. The algorithm laid a foundation for the realization of seed cotton cleaning detection of intelligent control.
(3)The seed cotton cleaning mechanism and the existing cleaning equipments were analyzed. The dissertation found that different types of equipments mainly cleaned different types of impurities. The cleaning efficiency of same type equipment was different in different cleaning process. According to this characteristic, this dissertation would all seed cotton cleaning equipment as a whole, and in accordance with the process of sorting. The factors which could affect the cleaning efficiency of seed cotton were analyzed. These factors mainly included the barbed nail roller speed of the two inclined seed cotton cleaners, the sawtooth roller speed of stripper and stick cleaner, the barbed nail roller speed of inclined and recovery seed cotton cleaner. This dissertation designed an orthogonal rotation experiment based on these factors. The control objectives included the reduction of fiber length, the increment of short fiber, the reduction of ginning outturn and the cleaning efficiency of different types impurities. By the results of this experiment, multi-objective optimization control model of seed cotton cleaning was established. The seed cotton cleaning multi-objective optimal control was realized.
(4)According to the seed cotton drying multi-objective optimal control model and the seed cotton cleaning multi-objective optimal control model, this dissertation designed a seed cotton pretreatment intelligent control system. The hardware structure of this system adopted3layers structure, including the management layer, control layer and field. Networking between each layer used the industrial Ethernet and field bus combination. The control strategy of this system was an intelligent control strategy. This intelligent control strategy based on the seed cotton drying multi-objective optimal control model and the seed cotton cleaning multi-objective optimal control model. The core algorithm of this intelligent control strategy based on chaotic particle swarm optimization algorithm. The algorithm grouped and initialized particle swarm, and improved the starting mechanism of chaos judgment. According to the actual production test, the intelligent control strategy could effectively control the seed pretreatment process. The parameters of seed cotton pretreatment equipment were optimized. Seed cotton moisture regain was more consistent. Seed cotton impurities content and the damage of cotton fibers were reduced.
The dissertation provided the theoretical and technical supports for seed cotton pretreatment process. The method provided a solution strategy to fine processing and helped to realize adjusting according to cotton properties. The study showed theoretical and practical significance to improve t cotton industry automation and intelligent level in China.
我国棉花种植密度很大,每亩可达到16000～18000株。过密的种植方式给籽棉清理工作带来很多难题。目前棉花采摘大多使用机械采摘方式,由于种植过密,很多棉枝、棉叶、铃壳无法脱落,跟随籽棉一起被采摘。因此棉花加工企业收购的籽棉含杂可高达到20%以上,这就加重了籽棉清理工作的负担。
目前我国棉花加工企业的籽棉预处理加工较为落后,主要体现在检测方式落后、加工设备智能化程度低等方面。当前棉花加工企业主要的加工方式是在加工前根据对籽棉的抽检结果调整籽棉预处理设备的关键参数,在加工过程中这些参数不再进行调整。这是一种粗犷的加工方式,设备运行参数无法根据籽棉性状进行实时调整,预处理后的籽棉回潮率一致性差、杂质含量高、棉纤维损伤大,严重影响了最终皮棉产品的质量。尤其是在加工的后期,新疆12月份的气候环境非常恶劣,严重影响了籽棉回潮率等性状,若按前期的加工方案和设备参数加工,则籽棉预处理后的含杂量和回潮率根本达不到轧花加工的要求。
针对这些问题,本文对籽棉预处理过程中的籽棉含杂量在线检测、籽棉烘干工艺、籽棉清理工艺等关键问题进行了研究,并设计了籽棉预处理智能控制系统。进行的主要工作如下：
(1)根据干燥理论,对籽棉烘干机理进行了研究分析。设计了籽棉热风烘干试验和微波烘干试验,通过试验结果对比分析,提出基于微波-热风联合烘干方式。通过设计的微波-热风联合烘干正交旋转试验建立了籽棉烘干多目标优化控制模型,改进后的烘干工艺有效克服了热风烘干延迟大、能耗大、污染大等缺陷,实现籽棉烘干的多目标优化控制。
(2)构建了一种籽棉含杂量在线检测装置,该装置主要由CCD工业相机、镜头、光源和采样机构组成。通过CCD工业相机对籽棉进行图像采样,为保证采样的准确性,该装置的光源必须能提供充足、均匀的光线。采样的图片经过以太网传递给上位机进行图像处理分析。本文在对大量籽棉和杂质采样照片的分析基础上提出了一种基于纹理的籽棉含杂量在线检测算法,该算法不仅可以精确计算出籽棉的含杂总量,还可以识别杂质的种类,以及每种杂质的含量,为实现籽棉清理智能控制奠定了检测基础。
(3)对籽棉清理机理及现有清理设备进行了分析,发现不同清理设备主要清理的杂质种类不同,同一清理设备在不同的清理工艺环节中清理效率不同。根据这一特点将所有籽棉清理设备按照工艺环节排序,并将其看成一个整体。分析提取了对棉纤维产生损伤和影响籽棉清理效率的因素,其中最主要的有：一级倾斜式籽棉清理机刺钉滚筒转速、提净式籽棉清理机锯齿滚筒转速、二级倾斜式籽棉清理机刺钉滚筒转速和回收式籽棉清理机刺钉滚筒转速,以这些因素为条件设计了正交旋转试验,通过试验结果建立了籽棉清理多目标优化模型,实现籽棉清理的多目标优化控制。
(4)根据本文建立的籽棉烘干和清理多目标优化模型以及籽棉预处理设备的特点设计了籽棉预处理智能控制系统。该系统的硬件结构采用管理层、监控层和现场层3层结构,各层之间采用工业以太网和现场总线相结合的组网技术。该系统软件的控制策略是在建立的籽棉烘干和清理多目标优化模型的基础上提出的籽棉预处理智能控制策略,该智能控制策略以混沌粒子群优化算法为基础,结合籽棉预处理过程的特点,将粒子群进行分组初始化,并对启动混沌机制的判断进行了改进,经过实际生产检验,该智能控制策略能对籽棉预处理过程进行有效控制,优化了预处理过程中各设备运行参数,使籽棉的回潮率一致性更好、含杂量更低、棉纤维损伤更小。
本文的研究为籽棉预处理加工提供了重要的理论和技术支持,为实现棉花精细化加工提供了解决方案,对提高我国棉花加工业自动化和智能化水平有较大的理论意义和实际应用价值。
From being picked to be processed into finished products, cotton generally experienced3processes. They are preparing, processing and packaging. Among them, the preparation process included seed cotton drying and cleaning, also known as the seed cotton pretreatment process. Seed cotton pretreatment process had two main tasks. The first was to adjust the seed cotton moisture regain. Seed cotton moisture regain most suitable for processing was6.5-8.5%. Low moisture regains made cotton fiber stiffness reduction. When being cleaned, the cotton fiber was easy to be broken. Excessive moisture regain would increase the friction between cotton fiber and impurities. So the impurities were not easy to be cleaned. The second was to clean impurities. Seed cotton should contain impurities as little as possible before being ginned. So the cotton processing enterprise should clean the impurities as much as possible in the seed cotton pretreatment process. But excessive cleaning would cause damage to cotton fibers. This could reduce the final quality of the cotton products
China's cotton planting density was large, which could reach16000～8000strain per mu. Many difficulties had product during the seed cleaning process because of the high planting density. At present most cotton was picked by harvester. Because of the high planting density, a lot of cotton plant, cotton leaf and boll shell could not take off. They were picked with seed cotton together. Therefore, the cotton processing enterprise purchased the cotton which had high impurities content. The maximum impurities content could reach more than20%. This problem aggravated the burden of seed cotton cleaning work.
Now, the seed cotton pretreatment process which was applied in cotton processing enterprises was relatively backward in our country. The problems mainly reflected in the detection way backward and low level of automation processing equipment. At present cotton processing enterprises mainly process was that adjust the key parameters of cotton seed processing equipment according to the sampling results before processing. These parameters were not adjusted in the process. This was a rough processing way. The key parameters of cotton seed processing equipment could not be real-time adjusted according to the seed cotton properties. Seed cotton which was pretreatment had a lot of problems. These problems included cotton moisture regain consistency, high impurity content and cotton fibers damage. All of these problems seriously affected the quality of the final cotton product. Especially in the late processing, the climatic environmental of Xinjiang is very poor in December. This serious influenced the properties of seed cotton. According to the early processing scheme and equipment parameters, the impurities content and moisture regain of seed cotton which were processed were not up to the requirements of ginning processing.
Aiming at these problems, this dissertation carried out research into on-line detection of seed cotton impurities content, seed cotton drying process, seed cotton cleaning process and designed the intelligent control system of seed cotton pretreatment. The main research contents of the dissertation area as follows:
(1)According to the drying theory, the seed cotton drying mechanism was analyzed. The seed cotton hot-air drying experiment and microwave drying experiment were designed. The main factors affecting the efficiency of drying included temperature of hot air and microwave power density. The control objectives included the drying efficiency, the damage of fiber specific breaking strength, the damage of Rd and the increment of +b. By analyzing the results of experiment, a new way based on microwave-hot air drying was proposed. The improved drying process effectively overcame the defects of hot-air drying, such as high energy consumption, big delay and pollution etc. By the orthogonal rotation experiment, multi-objective optimization control model of seed cotton drying was established. The seed cotton drying multi-objective optimal control was realized.
(2)A seed cotton impurities content on-line detection device was constructed. The device mainly consisted of CCD industrial camera, lens, the light source and the sampling mechanism. This device used the Pilot series area of black and white CCD industrial camera manufactured by Basler, type PiA2400-17gm, the CCTV series lenses manufactured by Myutron, type HF0528J. The focal length of lens was5mm, could be tuned to5.5mm. The light source used LED light, which color was white. The device sampled through the CCD industrial camera. In order to ensure the accuracy of the sampling, the device must be able to provide adequate and uniform light. Sampling images were transmitted to the host computer through Ethernet. Then the host computer analyzed these sampling images. By the analysis of seed cotton and impurities sampling images, the dissertation presented a cotton impurity content online detection algorithm based on texture. This algorithm not only could accurately calculate the impurity content of seed cotton, but also could identify the types of impurities. The algorithm laid a foundation for the realization of seed cotton cleaning detection of intelligent control.
(3)The seed cotton cleaning mechanism and the existing cleaning equipments were analyzed. The dissertation found that different types of equipments mainly cleaned different types of impurities. The cleaning efficiency of same type equipment was different in different cleaning process. According to this characteristic, this dissertation would all seed cotton cleaning equipment as a whole, and in accordance with the process of sorting. The factors which could affect the cleaning efficiency of seed cotton were analyzed. These factors mainly included the barbed nail roller speed of the two inclined seed cotton cleaners, the sawtooth roller speed of stripper and stick cleaner, the barbed nail roller speed of inclined and recovery seed cotton cleaner. This dissertation designed an orthogonal rotation experiment based on these factors. The control objectives included the reduction of fiber length, the increment of short fiber, the reduction of ginning outturn and the cleaning efficiency of different types impurities. By the results of this experiment, multi-objective optimization control model of seed cotton cleaning was established. The seed cotton cleaning multi-objective optimal control was realized.
(4)According to the seed cotton drying multi-objective optimal control model and the seed cotton cleaning multi-objective optimal control model, this dissertation designed a seed cotton pretreatment intelligent control system. The hardware structure of this system adopted3layers structure, including the management layer, control layer and field. Networking between each layer used the industrial Ethernet and field bus combination. The control strategy of this system was an intelligent control strategy. This intelligent control strategy based on the seed cotton drying multi-objective optimal control model and the seed cotton cleaning multi-objective optimal control model. The core algorithm of this intelligent control strategy based on chaotic particle swarm optimization algorithm. The algorithm grouped and initialized particle swarm, and improved the starting mechanism of chaos judgment. According to the actual production test, the intelligent control strategy could effectively control the seed pretreatment process. The parameters of seed cotton pretreatment equipment were optimized. Seed cotton moisture regain was more consistent. Seed cotton impurities content and the damage of cotton fibers were reduced.
The dissertation provided the theoretical and technical supports for seed cotton pretreatment process. The method provided a solution strategy to fine processing and helped to realize adjusting according to cotton properties. The study showed theoretical and practical significance to improve t cotton industry automation and intelligent level in China.
引文
[1]http://www.china-cotton.org
[2]刘向新,周亚立,梅建,等.新疆棉花清理加工技术条件[J].中国棉花加工.2006,(3)：9-10.
[3]Byler R K. Historical Review on the Effect of Moisture Content and the Addition of Moisture to Seed Cotton before Ginning on Fiber Length [J]. The Journal of Cotton Science.2006,10(4): 300-310.
[4]Hardin IV Robert G, Searcy Stephen W. Viscoelastic Properties of Seed Cotton[J]. Transactions of the ASABE.2008,51(3):803-810.
[5]Byler Richard K, Boykin J Clif. Seed Cotton Moisture Conditioning Using an Atomizing Nozzle in the Conveyer-distributor[J]. Applied Engineering in Agriculture.2006,22(6): 819-826.
[6]Baker K D, Hughs E. A Survey of Seed Cotton Dryers in Cotton Gins in the Southwestern United States[J]. Applied Engineering in Agriculture.2012,28(1):87-97.
[7]Laird Weldon, Baker Roy V. Potential of New Seed Cotton Cleaning Principles[J]. Transactions of the American Society of Agricultural.1984,27(1):205-208.
[8]Whitelock Derek P, Anthony W Stanley. Evaluation of Cylinder Cleaner Grid Bar Configuration and Cylinder Speed for Cleaning of Seed Cotton, Lint, and Cleaner Waste[J]. Applied Engineering in Agriculture.2003,19(1):31-37.
[9]Patil P G, Anap G R, Arude V G. Design and Development of Cylinder Type Cotton Pre-cleaner[J]. AMA, Agricultural Mechanization in Asia, Africa and Latin America.2006, 37(3):46-51.
[10]Bel Patricia Damian, Xu Bugao, Debbie. Automatic Detection of Seed Coat Fragments in Cotton Fabrics[J]. Textile Research Journal.2012,82(16):1711-1719.
[11]Hardin IV R G, Searcy S W. Autonomous Cotton Module Forming System[J]. Applied Engineering in Agriculture.2011,27(4):559-568.
[12]Anthony W S. Methods to Reduce Lint Cleaner Waste and Damage[J]. Transactions of the American Society of Agricultural Engineers.2000,43(2):221-229.
[13]http://www.uster.com/cn/
[14]http://www.samjackson.com
[15]李建民.新疆籽棉烘干、热源及其相关标准问题综述[J].中国棉花加工.2011,(1)：32-34.
[16]吴国新,车志新.棉包回潮率在线测试系统的研制[J].中国纤检.2006,(1)：30-32.
[17]杨海军,李顺利,张霖.籽棉回潮率在线监测技术研究[J].中国棉花加工.2009,(6)：40-42.
[18]陈玉辉,彭根旺.影响籽棉清理机清杂效率的因素[J].中国棉花加工.2008,(6)：7-9.
[19]虞华,房兰萍,陈东胜.几种籽棉清理部件的分析[J].中国棉花加工.2004,(2)：15-16.
[20]郭斌杰,王泽武,向天明,等.具有烘干功能的高效机采籽棉清理机的设计[J].中国棉花加工.2011,(4)：24-28.
[21]冯显英,任长志,解守华.基于阀岛技术的异性纤维清除系统结构与设计[J].机床与液压.2006,(11)：139-140.
[22]李伟,郑小梅.籽棉烘干系统自动化控制设计[J].中国棉花加工.2010,(4)：10-14.
[23]张成梁.棉花加工过程智能化关键技术研究[D].济南：山东大学,博十学位论文,2011.
[24]杨文柱,李道亮,魏新华,等.基于自动视觉检测的棉花异性纤维分类系统[J].农业机械学报.2009,40(12)：177-227.
[25]Zhang Xin, Li Daoliang, Yang Wenzhu, et al. An Improved Morphological Edge Detection Method for Color Image of Cotton Foreign Fibers[J]. Sensor Letters.2011,9(3):1020-1023.
[26]周阳,朱邦太,李勋,等.一种基于视频技术的棉花异性纤维分拣方法[J].河南科技大学学报(自然科学版).2008,29(2)：90-93.
[27]罗德坡,朱邦太,李勋.紫外线荧光效应及其在棉花异性纤维分拣系统中的应用[J].河南科技大学学报(自然科学版).2008,28(2)：63-66.
[28]郑文秀,刘双喜,魏新华,等.基于Mean-shift的棉花异性纤维图像分割[J].山东农业大学学报(自然科学版).2009,40(2)：224-228.
[29]潘永康.现代干燥技术[M].北京：化学工业出版社,1998.
[30]GB/T 6102.2-2009.原棉回潮率实验方法电测器法[S].北京：中国标准出版社,2009.
[31]杨超,刘军民.籽棉干燥机经济运行分析[J].中国棉花加工.2009,(4)：13-15.
[32]谭东,刘斌.籽棉烘干系统在棉花加工中的应用[J].农村新技术.2010,(6)：22-23.
[33]张红战.回潮率对棉花加工工艺系统的影响[J].中国棉花加工.2008,(4)：19-21.
[34]鲁文丁.棉花加工企业如何提高机采皮棉质量[J].中国棉花加工.2007,(5)：33-35.
[35]孙玉杰.浅谈籽棉预处理对棉花品质的影响[J].中国纤检.2007,(8)：52-53.
[36]Hajek Milan, Durovic Michal, Paulusova Hana, et al. Simultaneous Microwave Drying and Disinfection of Flooded Books[J]. Restaurator.2011,32(1):1-12.
[37]Prasad B E, Pandey Krishna K. Microwave Drying of Bamboo[J]. European Journal of Wood and Wood and Wood Products.2012,70(1-3):353-355.
[38]Bal Lalit M, Kar Abhijit, Satya Santosh, et al. Drying Kinetics and Effective Moisture Diffusivity of Bamboo Shoot Slices Undergoing Microwave Drying[J]. International Journal of Food Science and Technology.2010,45(11):2321-2328.
[39]Skubic Blaz, Lakner Mitja, Plazl lgor. Microwave Drying of Expanded Perlite Insulation Board[J]. Industrial and Engineering Chemistry Research.2012,51(8):3314-3321.
[40]Nair Gopu Raveendran, Li Zhenfeng, Gariepy Yvan, et al. Microwave Drying of Corn(Zea Mays L. ssp.) for the Seed Industry[J]. Drying Technology.2011,29(11):1291-1-296.
[41]Antti Lena, Finell Michael, Arshadi Mehrdad, et al. Effects of Microwave Drying on Biomass Fatty Acid Composition and Fuel Pellet Quality[J]. Wood Material Science and Engineering. 2011,6(1-2):34-40.
[42]陈德经.干燥方法对金银花的质量影响研究[J].食品科学.2006,27(11)：277-279.
[43]黄建立.银耳微波真空干燥机理及品质特性的研究[D].福州：福建农林大学,博士学位论文,2010.
[44]钟成义,肖宏儒,秦广明.枸杞子微波热风联合干燥技术研究[J].农业装备技术.2010,36(4)：10-13.
[45]曾绍校,梁静,郑宝东,等.不同干燥工艺对莲子品质的影响[J].农业工程学报.2007,23(5)：227-231.
[46]章斌,侯小桢.热风与微波联合干燥香蕉片的工艺研究[J].食品与机械.2010,26(2)：97-99.
[47]Khraisheh N A M, Cooper T J R, Magect R A. Shrinkage Characteristics of Potatoes Dehydrated Under Combined Microwave and Convective Air Conditions Drying[J]. Technology International.1997,15:1003-1022.
[48]Khraisheh M A, McMinn W A, Magee T R. Quality and Structural Changes in Starchy Foods During Microwave and Conventive Drying[J]. Food Research International.2004,37(5): 497-503.
[49]Emel Iraz Goksu, Gulum Sumnu, Ali Esin. Effect of Microwave on Fluidized Bed Drying of Macaroni Beads[J]. Journal of Food Engineering.2005, (66):463-468.
[50]周韵.热风微波耦合干燥特性研究[D].无锡：江南大学,硕十学位论文,2011.
[51]Vongpradubchai S, Rattanadecho P. Microwave and Hot Air Drying of Wood Using a Rectangular Waveguide[J]. Drying Technology.2011,29(4):451-460.
[52]Saxena Alok, Maity Tanushree, Raju P S, et al. Degradation Kinetics of Colour and Total Carotenoids in Jackfruit (Artocarpus Heterophyllus) Bulb Slices During Hot Air Drying[J]. Food and Bioprocess Technology.2012,5(2):672-679.
[53]Kumar Navneet, Sarkar B C, Sharma H K. Mathematical Modelling of Thin Layer Hot Air Drying of Carrot Pomace[J]. Journal of Food Science and Technology.2012,49(1):33-41.
[54]Hiranvarachat Bhudsawan, Devahastin Sakamon, Chiewchan Naphaporn. Effects of Acid Pretreatments on Some Physicochemical Properties of Carrot Undergoing Hot Air Drying[J]. Food and Bioproducts Processing.2011,89(2):116-127.
[55]Sturm Barbara, Hofacker Werner C, Hensel Oliver. Optimizing the Drying Parameters for Hot-Air-Dried Apples[J]. Drying Technology.2012,30(14):1570-1582.
[56]Akbudak Nuray, Akbudak Bulent. Effect of Vacuum, Microwave, and Convective Drying on Selected Parsley Quality[J]. International Journal of Food Properties.2013,16(1):205-215.
[57]Ganesapillai Magesh, Regupathi Lyyaswami, Murugesan Thanapalan. Characterization and Process Optimization of Microwave Drying of Plaster of Paris[J]. Drying Technology.2008, 26(12):1484-1496.
[58]Li Z Y, Wang R F, Kudra T. Uniformity Issue in Microwave Drying[J]. Drying Technology. 2011,29(6):652-660.
[59]Bal Lalit M, Kar Abhijit, Satya Santosh, et al. Kinetics of Colour Change of Bamboo Shoot Slices During Microwave Drying[J]. International Journal of Food Science and Technology. 2011,46(4):827-833.
[60]Motevali Ali, Minaei Saeid, Khoshtagaza Mohammad Hadi. Evaluation of Energy Consumption in Different Drying Methods[J]. Energy Conversion and Management.2011, 52(2):1192-1199.
[61]Dev S R S, Geetha P, Orsat V, et al. Effects of Microwave-assisted Hot Air Drying and Conventional Hot Air Drying on the Drying Kinetics, Color, Rehydration, and Volatiles of Moringa Oleifera[J]. Drying Technology.2011,29(12):1452-1458.
[62]Stephen A F, Charles R H. IntelliGin-A Ginning Revolution from Process Control Technology[J]. International Food and Agribusiness Management Review.1998,1(4): 555-566.
[63]Wiegand Christian, Herrmann Michael, Bachtier Sebastian, et al. APulsed THz Imaging System with a Line Focus and a Balanced 1-D Detection Scheme with Two Industrial CCD Line-scan Cameras[J]. Optics Express.2010,18(6):5595-5601.
[64]Honkanen Markus, Eloranta Hannu, Saarenrinne Pentti. Digital Imaging Measurements of Dense Multiphase Flows in Industrial Processes[J]. Flow Measurement and Instrumentation. 2010,21(1):25-32.
[65]Coates Colin, Campillo Chris. CCDs Lose Ground to New CMOS Sensors[J]. Laser Focus World.2011,47(3):40-45.
[66]Grace Carl R, Walder Jean-Pierre, Denes Peter, et al. Multiplexed Oversampling Digitizer in 65 nm CMOS for Column-Parallel CCD Readout[J]. IEEE Transactions on Nuclear Science. 2013,60(1):246-250.
[67]Aalsalem Mohammed Y, Khan Wazir Zada, Arshad Quratul Ain. A Low Cost Vision Based Hybrid Fiducial Mark Tracking Technique for Mobile Industrial Robots[J]. International Journal of Computer Science Issues.2012,9(44-2):151-156.
[68]王炜峰.数字图像处理技术在熟料窑监控系统中的应用[J].计算机测量与控制.2012,10(8)：511-513.
[69]柴阿丽.基于计算机视觉和光谱分析技术的蔬菜叶部病害诊断研究[D].北京：中国农业 科学院,博士学位论文,2011.
[70]吴兰兰.基于数字图像处理的玉米苗期田间杂草的识别研究[D].武汉：华中农业大学,博士学位论文,2010.
[71]Nagatani Keiji, Ikeda Ayako, Ishigami Genya, et al. Development of a Visual Odometry System for a Wheeled Robot on Loose Soil Using a Telecentric Camera[J]. Advanced Robotics. 2010,24(8-9):1149-1167.
[72]Ferria Kouider, Laouar Naamane, Bouaouadja Noureddine. Acousto-optic Method for Liquids Refractometry[J]. Optica Applicata.2011,41 (1):109-119.
[73]Florin Toadere, Mastorakis Nikos E. Simulation the Functionality of a Laser Pulse Image Acquisition System[J]. WSEAS Transactions on Circuits and System.2010,9(1):22-31.
[74]Lenk Felix, Vogel Mathias, Bley Thomas, et al. Automatic Image Recognition to Determine Morphological Development and Seconday Metabolite Accumulation in Hairy Root Networks[J]. Engineering in Life Sciences.2012,12(6):588-594.
[75]刘伟华.基于机器视觉的煤尘在线监测系统关键技术研究[D].济南：山东大学,博士学位论文,2011.
[76]魏新华.水果机器视觉自动分选机关键技术研究[D].南京：东南大学,博士学位论文,2008.
[77]李刚.基于图像工程的路面破损自动识别算法研究[D].西安：长安大学,博十学位论文,2010.
[78]王松磊.红枣自动分级分选机的研制[D].银川：宁夏大学,硕十学位论文,2009.
[79]Hong Byung-Joo, Park Chan-Oh, Seo Nam-Seok. A Real-time Compact Structured-light Based Range Sensing System[J]. Journal of Semiconductor Technology and Science.2012,12(2): 193-202.
[80]Bruno F, Bianco G, Muzzupappa M, et al. Experimentation of Structured Light and Stereo Vision for Underwater 3D Reconstruction[J].ISPRS Journal of Photogrammetry and Remote Sensing.2011,66(4):508-518.
[81]Barone Sandro, Paoli Alessandro, Razionale Armando Viviano. Three-dimensional Point Cloud Alignment Detecting Fiducial Markers by Structured Light Stereo Imaging[J]. Machine Vision and Applications.2012,23(2):217-229.
[82]Marsi Stefano, Ramponi Giovanni. A Flexible FPGA Implementation for Illuminance-reflectance Video Enhancement[J]. Journal of Real-time Image Processing.2013, 8(1):81-93.
[83]陆秋琰,陈坤杰.牛肉图像采集光照系统的设计与研究[J].农机化研究.2008,(6)：78-81.
[84]杨文柱,李道亮,魏新华,等.基于改进遗传算法的棉花异性纤维目标特征选择[J].农业机械学报.2010,41(4)：173-178.
[85]刘双喜,张馨,郑文秀,等.棉花异性纤维图像特征提取[J].农业机械学报,2010.41(3)：158-163.
[86]李国辉,苏真伟,夏心怡.基于不规则成像机器视觉的棉花白色异纤检测算法[J].农业机械学报.2010,41(5)：164-167.
[87]Nalpantidis Lazaros, Gasteratos A. Stereo Vision of Robotic Applications in the Presence of Non-ideal Lighting Conditions[J]. Liage and Vision Computing.2010,28(6):940-951.\
[88]Sun J, Smith M, Smith L, et al. Illumination Compensation for Nominally Planar Surface Recovery[J]. IET Computer Vision.2012,6(5):371-377.
[89]Muruganantham S, Jebarajan T. An Efficient Face Recognition System Based on the Hybridization of Pose Invariant and Illumination Process[J]. International Journal of Computer Science Issues.2012,9(44-1):228-237.
[90]Fouad Mohamed M, Dansereau Richard M, Whitehead Anthony D. Image Registration under Illumination Variations Using Region-based Confidence Weighted M-estimators[J]. IEEE Transactions on Imaged Processing.2012,21(3):1046-1060.
[91]Bong C W, Rajeswari M. Multiobjective Clustering With Metaheuristic: Current Trends and Methods in Image Segmentation[J]. IET Image Processing.2012,6(1):1-10.
[92]Acqua F D, Gamba P, Trianni G. Semi-automatic Choice of Acaledependent Features for Sateiilte SAR Image Classification[J]. Pattern Recognition Letters.2006,27(4):244-251.
[93]李洪艳,曹建荣,谈文婷,等.图像分割技术综述[J].山东建筑大学学报.2010,25(1)：85-89.
[94]Mayszko Dariusz, Stepaniuk Jarosaw. Adaptive Rough Entropy Clustering Algorithms in Image Segmentation[J]. Fundamenta Imformaticae.2010, 98(2-3):199-231.
[95]Marques Regic C Pinheiro, Medeiros Ftima N, Santos Nobre Juvencio. SAR Image Segmentation Based on Level Set Approach and GOA Model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2012,34(10):2046-2057.
[96]许凯,秦昆,黄伯和,等.基于云模型的图像区域分割方法[J].中国图象图形学报.2010,15(5)：757-763.
[97]谭洪波,侯志强,刘荣.基于人类视觉模型的区域生长图像分割[J].中国图象图形学报.2010,15(9)：1352-1356.
[98]Udupa J K, Saha P K, Lotufo R A. Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(11):1485-1500.
[99]龚永义,黄辉,于继明,等.基于熵的两区域图像分割[J].中国图象图形学报,2011,16(5)：754-760.
[100]Etemad S Ali, White Tony. An Ant-inspired Algorithm for Detection of Image Edge Features[J]. Applied Soft Computing Journal.2011,11(8):4883-4893.
[101]Kaur Amandeep, Singh Chandan. Sub-pixel Edge Detection Using Pseudo Zernike Moment[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition.2011, 4(2):107-118.
[102]Direkolu Cem, Nixon Mark S. Moving-edge Detection Via Heat Flow Analogy[J]. Pattern Recognition Letters.2011,32(2):270-279.
[103]Monaco James P, Madabhushi Anant. Class-specific Weighting for Markov Random Field Estimation: Application to Medical Image Segmentation[J]. Medical Image Analysis.2012, 16(8):1477-1489.
[104]Yousefi S, Kehtarnavaz N, Cao Y, et al. Bilateral Markov Mesh Random Field and Its Application to Image Restoration[J]. Journal of Visual Communication and Image Representation.2012,23(7):1051-1059.
[105]Abbasgholipour M, Omid M, Keyhani A, et al. Color Image Segmentation with Genetic Algorithm in a Raisin Sorting System Based on Machine Vision in Variable Conditions[J]. Expert Systems with Applications.2011,38(4):3671-3678.
[106]Ahmed Raghad Jawad. Genetic-based Multi Resolution Noisy Color Image Segmentation[J]. World Academy of Science, Engineering and Technology.2010,45,573-579.
[107]Chen Chieh-Li, Tai Chung-Li. Adaptive Fuzzy Color Segmentation with Neural Network for Road Detections[J]. Engineering Applications of Artificial Intelligence.2010,23(3):400-410.
[108]Baykan Nurdan Akhan, Yilmaz Nihat. Mineral Identification Using Color Spaces and Artifical Neural Networks[J]. Computers and Geosciences.2010,36(1):91-97.
[109]Nguyen Thi Nhat Anh, Cai Jianfei, Zhang Juyong, et al. Robust Interactive Image Segmentation Using Convex Active Contours[J]. IEEE Transactions on Image Processing. 2012,21(8):3734-3743.
[110]Kovacs Andrea, Sziranyi Tamas. Harris Function Based Active Contour External Force for Image Segmentation[J]. Pattern Recognition Letters.2012,33(9):1180-1187.
[111]Sklansky J, Image Segmentation and Fezture Extraction[J]. IEEE Transactions on Systems, Man, and Cybernetics.1978,8(5):237-247.
[112]Haralick R M. Statistical and Structural Approaches to Texture[J]. Proceedings of the IEEE. 1979,76(5):786-804.
[113]刘晓民.纹理研究及其应用综述[J].测控技术.2008,27(5)：4-9.
[114]孙君顶,马媛嫒.纹理特征研究综述[J].计算机系统应用.2010,19(6)：245-250.
[115]Haralick R M, Shanmugam K, Dinstein Ⅰ. Textural Features for Image Classification[J]. IEEE Transactions on System, Man, and Cybernetics.1973,3(6):610-621.
[116]Ulaby F T, Kouyate F, Brisco B, et al. Textural Information in SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing.1986,24(2):235-245.
[117]Baraldi A, Parmiggiani F. An Investigation of the Textual Characteristics Associated with Gray Level Co-ocurrenee Matrix Statistical Parameters[J]. IEEE Transactions on Geoscience and Remote Sensing.1995,33(2):293-304.
[118]薄华,马缚龙,焦李成.图像纹理的灰度共生矩阵计算问题的分析[J].电子学报.2006,34(1)：155-158.
[119]Clif Boykin J, Bechere Efrem, Meredith Jr, et al. Engineering and Ginning Cotton Genotype Differences in Fiber-seed Attachment Force[J]. Journal of Cotton Science.2012,16(3): 170-178.
[120]Barker G L. Analysis of Seed Cotton Cleaning Efficiency Using GINQUAL[J]. Applied Engineering in Agriculture.1991,7(6):667-672.
[121]Gillum M N, Armijo C B. Pima Seed Cotton Cleaning for Maximum Profit[J]. Transactions of the American Society of Agricultural Engineers.1997,40(3):513-518.
[122]Laird W, Columbus E P, Bragg C K. Seed Cotton Quality Separation by a Mechanical Cleaner[J]. Transactions of the American Society of Agricultural Engineers.1991,34(3): 718-726.
[123]Wanjura John D, Gamble Gary R, Holt Gregory A, et al. Influence of Grid Bar Shape on Field Cleaner Performance-field Testing[J]. Journal of Cotton Science.2012,16(4):255-267.
[124]Liu Gui, Yang Yu-Rong, Wang Ming-kui, et al. Comprehensive Characterization Model of Integrated Cotton Fiber Quality Index[J]. Journal of Donghua University(Engilsh Edition). 2011,28(4):379-383.
[125]Chaiba A, Abdessemed R, Bendaas M L. A Hybrid Intelligent Control Based Torque Tracking Approach for Doubly Fed Asynchronous Motor(DFAM) Drive[J]. Journal of Electrical Systems.8(3):262-272.
[126]Aleksendric Dragan, Jakovljevic Zivana, Cirovic Velimir. Intelligent Control of Braking Process[J]. Expert Systems with Applications.39(14):11758-11765.
[127]Farivar Faezeh, Shoorehdeli Mahdi Aliyari, Teshnehlab Mohammad. An Interdisciplinary Overview and Intelligent Control of Human Prosthetic Eye Movements System for the Emotional Support by a Huggable Pet-type Robot From a Biomechatronical Viewpoint[J]. Journal of the Franklin Institute.2012,349(7):2243-2267.
[128]Prakash Raghupathy, Anita Rajapalan. Robust Model Reference Adaptive Intelligent Control[J]. International Journal of Control, Automation and Systems.2012,10(2):396-406.
[129]Pontani Mauro, Conway Bruce A. Particle Swarm Optimization Applied to Implusive Orbtial Transfers[J]. Acta Astronautica.2012,74:141-155.
[130]Chalennchaiarbha Saksorn, Ongsakul Weerakorn. Elitist Multi-objective Particle Swarm Optimization with Fuzzy Multi-attribute Decision Making for Power Dispatch[J]. Electric Power Components and Systems.2012,40(14):1562-1585.
[131]Tewolde Girma S, Hanna Darrin M, Haskell Richard E. A Modular and Efficient Hardware Architecture for Particle Swarm Optimization Algorithm[J]. Microprocessors and Microsystems.2012,36(4):289-302.
[132]Shayeghi H, Shayanfar H A, Jalizadeh S, et al. Multi-machine Power System Stabilizers Design Using Chaotic Optimization Algorithm[J]. Energy Conversion and Management.2010, 51(7):1572-1580.
[133]Ahmadi Mohamadreza, Mojallali Hamed. Chaotic Invasive Weed Optimization Algorithm with Application to Parameter Estimation of Chaotic Systems[J]. Chaos, Solitions and Fractals. 2012,45(9-10):1108-1120.
[134]Okamoto Takashi, Hirata Hironori. Constrained Optimization Using a Multipoint Type Chaotic Lagrangian Method with a Coupling Structure[J]. Engineering Optimization.2013,45(3): 311-336.
[135]Tatsumi Keiji, Ibuki Takeru, Tanino Tetsuzo. A Chaotic Particle Swarm Optimization Exploiting a Virtual Quartic Objective Function Based on the Personal and Global Best Solutions[J]. Applied Mathematics and Computation.2013,219(17):8991-9011.
[136]Mukhopadhyay Sumona, Banerjee Santo. Global Optimization of an Optical Chaotic System by Chaotic Multi Swarm Particle Swarm Optimization[J]. Expert Systems with Applications. 2012,39(1):917-924.
[137]Alfi Alireza. Particle Swarm Optimization Algorithm with Dynamic Inertia Weight for Online Parameter Identification Applied to Lorenz Chaotic System[J]. International Journal of Innovative Computing, Information and Control.2012,8(2):1191-1203.
[138]Shirazi Masoud Jahromi, Vatankhah Ramin, Boroushaki Mehrdad, et al. Application of Particle Swarm Optimization in Chaos Synchronization in Noisy Environment in Presence of Unknown Parameter Uncertainty[J]. Communications in Nonlinear Science and Numerical Simulation. 2012,17(2):742-753.
[139]Firouzi Bahman Bahmani, Meymand Hamed Zeinoddini, Niknam Taher, et al. A Novel Multi-objective Chaotic Crazy PSO Algorithm for Optimal Operation Management of Distribution Network with Regard to Fuel Cell Power Plants[J]. International Journal of Innovative Computing, Information and Control.7(11):6395-6409.
[140]Sadeghpour Mehdi, Salarieh Hassan, Alasty Aria. Minimum Entropy Control of Chaos Via Online Particle Swarm Optimization Method[J]. Applied Mathematical Modelling.2012,36(8): 3931-3940.
[141]Safari A, Shayeghi H. Iteration Particle Swarm Optimization Procedure for Economic Load Dispatch with Generator Constraints[J]. Expert Systems with Applications.38(5):6043-6048.
[142]Coelho Leandro Dos Santos, Mariani Viviana Cocco. Firefly Algorithm Approach Based on Chaotic Tinkerbell Map Applied to Multivariable PID Controller Tuning[J], Computers and Mathematics with Applications.2012,64(8):2371-2382.
[143]Singh Manas Ranjan, Mahapatra S S. A Swarm Optimization Approach for Flexible Flow Shop Scheduling with Multiprocessor Tasks[J]. International Journal of Advanced Manufacturing Technology.2012,62(1-4):267-277.
[144]盛煜翔,潘海天,夏陆岳,等.混合混沌粒子群算法在苯与甲苯闪蒸过程优化中的应用[J].浙江工业大学学报,2010,38(3)：318-321.
[145]朱求红.锌冶炼除钴过程建模与智能优化方法研究及应用[D].长沙：中南大学,博士学位论文,2010
[146]Sheikhan Mansour, Shahnazi Reza, Garoucy Sahar. Synchronization of General Chaotic Systems Using Neural Controllers with Application to Secure Communication[J]. Neural Computing and Applications.2013,22(2):361-373.
[147]Abedini Mohammad, Vatankhah Ramin, Assadian Nima. Stabilizing Chaotic System on Periodic Orbits Using Multi-interval and Modem Optimal Control Strategies[J]. Communications in Nonlinear Science and Numerical Simulation.2012,17(10):3832-3842.
[148]Chaiba A, Abdessemed R, Bendaas M L. A Hybrid Intelligent Control Based Torque Tracking Approach for Doubly Fed Asynchronous Motor(DFAM) Drive[J]. Journal of Electrical Systems.8(3):262-272.
[149]Aleksendric Dragan, Jakovljevic Zivana, Cirovic Velimir. Intelligent Control of Braking Process[J]. Expert Systems with Applications.39(14):11758-11765.
[150]Farivar Faezeh, Shoorehdeli Mahdi Aliyari, Teshnehlab Mohammad. An Interdisciplinary Overview and Intelligent Control of Human Prosthetic Eye Movements System for the Emotional Support by a Huggable Pet-type Robot From a Biomechatronical Viewpoint[J]. Journal of the Franklin Institute.2012,349(7):2243-2267.
[151]Prakash Raghupathy, Anita Rajapalan. Robust Model Reference Adaptive Intelligent Control[J]. International Journal of Control, Automation and Systems.2012,10(2):396-406.
[2]刘向新,周亚立,梅建,等.新疆棉花清理加工技术条件[J].中国棉花加工.2006,(3)：9-10.
[3]Byler R K. Historical Review on the Effect of Moisture Content and the Addition of Moisture to Seed Cotton before Ginning on Fiber Length [J]. The Journal of Cotton Science.2006,10(4): 300-310.
[4]Hardin IV Robert G, Searcy Stephen W. Viscoelastic Properties of Seed Cotton[J]. Transactions of the ASABE.2008,51(3):803-810.
[5]Byler Richard K, Boykin J Clif. Seed Cotton Moisture Conditioning Using an Atomizing Nozzle in the Conveyer-distributor[J]. Applied Engineering in Agriculture.2006,22(6): 819-826.
[6]Baker K D, Hughs E. A Survey of Seed Cotton Dryers in Cotton Gins in the Southwestern United States[J]. Applied Engineering in Agriculture.2012,28(1):87-97.
[7]Laird Weldon, Baker Roy V. Potential of New Seed Cotton Cleaning Principles[J]. Transactions of the American Society of Agricultural.1984,27(1):205-208.
[8]Whitelock Derek P, Anthony W Stanley. Evaluation of Cylinder Cleaner Grid Bar Configuration and Cylinder Speed for Cleaning of Seed Cotton, Lint, and Cleaner Waste[J]. Applied Engineering in Agriculture.2003,19(1):31-37.
[9]Patil P G, Anap G R, Arude V G. Design and Development of Cylinder Type Cotton Pre-cleaner[J]. AMA, Agricultural Mechanization in Asia, Africa and Latin America.2006, 37(3):46-51.
[10]Bel Patricia Damian, Xu Bugao, Debbie. Automatic Detection of Seed Coat Fragments in Cotton Fabrics[J]. Textile Research Journal.2012,82(16):1711-1719.
[11]Hardin IV R G, Searcy S W. Autonomous Cotton Module Forming System[J]. Applied Engineering in Agriculture.2011,27(4):559-568.
[12]Anthony W S. Methods to Reduce Lint Cleaner Waste and Damage[J]. Transactions of the American Society of Agricultural Engineers.2000,43(2):221-229.
[13]http://www.uster.com/cn/
[14]http://www.samjackson.com
[15]李建民.新疆籽棉烘干、热源及其相关标准问题综述[J].中国棉花加工.2011,(1)：32-34.
[16]吴国新,车志新.棉包回潮率在线测试系统的研制[J].中国纤检.2006,(1)：30-32.
[17]杨海军,李顺利,张霖.籽棉回潮率在线监测技术研究[J].中国棉花加工.2009,(6)：40-42.
[18]陈玉辉,彭根旺.影响籽棉清理机清杂效率的因素[J].中国棉花加工.2008,(6)：7-9.
[19]虞华,房兰萍,陈东胜.几种籽棉清理部件的分析[J].中国棉花加工.2004,(2)：15-16.
[20]郭斌杰,王泽武,向天明,等.具有烘干功能的高效机采籽棉清理机的设计[J].中国棉花加工.2011,(4)：24-28.
[21]冯显英,任长志,解守华.基于阀岛技术的异性纤维清除系统结构与设计[J].机床与液压.2006,(11)：139-140.
[22]李伟,郑小梅.籽棉烘干系统自动化控制设计[J].中国棉花加工.2010,(4)：10-14.
[23]张成梁.棉花加工过程智能化关键技术研究[D].济南：山东大学,博十学位论文,2011.
[24]杨文柱,李道亮,魏新华,等.基于自动视觉检测的棉花异性纤维分类系统[J].农业机械学报.2009,40(12)：177-227.
[25]Zhang Xin, Li Daoliang, Yang Wenzhu, et al. An Improved Morphological Edge Detection Method for Color Image of Cotton Foreign Fibers[J]. Sensor Letters.2011,9(3):1020-1023.
[26]周阳,朱邦太,李勋,等.一种基于视频技术的棉花异性纤维分拣方法[J].河南科技大学学报(自然科学版).2008,29(2)：90-93.
[27]罗德坡,朱邦太,李勋.紫外线荧光效应及其在棉花异性纤维分拣系统中的应用[J].河南科技大学学报(自然科学版).2008,28(2)：63-66.
[28]郑文秀,刘双喜,魏新华,等.基于Mean-shift的棉花异性纤维图像分割[J].山东农业大学学报(自然科学版).2009,40(2)：224-228.
[29]潘永康.现代干燥技术[M].北京：化学工业出版社,1998.
[30]GB/T 6102.2-2009.原棉回潮率实验方法电测器法[S].北京：中国标准出版社,2009.
[31]杨超,刘军民.籽棉干燥机经济运行分析[J].中国棉花加工.2009,(4)：13-15.
[32]谭东,刘斌.籽棉烘干系统在棉花加工中的应用[J].农村新技术.2010,(6)：22-23.
[33]张红战.回潮率对棉花加工工艺系统的影响[J].中国棉花加工.2008,(4)：19-21.
[34]鲁文丁.棉花加工企业如何提高机采皮棉质量[J].中国棉花加工.2007,(5)：33-35.
[35]孙玉杰.浅谈籽棉预处理对棉花品质的影响[J].中国纤检.2007,(8)：52-53.
[36]Hajek Milan, Durovic Michal, Paulusova Hana, et al. Simultaneous Microwave Drying and Disinfection of Flooded Books[J]. Restaurator.2011,32(1):1-12.
[37]Prasad B E, Pandey Krishna K. Microwave Drying of Bamboo[J]. European Journal of Wood and Wood and Wood Products.2012,70(1-3):353-355.
[38]Bal Lalit M, Kar Abhijit, Satya Santosh, et al. Drying Kinetics and Effective Moisture Diffusivity of Bamboo Shoot Slices Undergoing Microwave Drying[J]. International Journal of Food Science and Technology.2010,45(11):2321-2328.
[39]Skubic Blaz, Lakner Mitja, Plazl lgor. Microwave Drying of Expanded Perlite Insulation Board[J]. Industrial and Engineering Chemistry Research.2012,51(8):3314-3321.
[40]Nair Gopu Raveendran, Li Zhenfeng, Gariepy Yvan, et al. Microwave Drying of Corn(Zea Mays L. ssp.) for the Seed Industry[J]. Drying Technology.2011,29(11):1291-1-296.
[41]Antti Lena, Finell Michael, Arshadi Mehrdad, et al. Effects of Microwave Drying on Biomass Fatty Acid Composition and Fuel Pellet Quality[J]. Wood Material Science and Engineering. 2011,6(1-2):34-40.
[42]陈德经.干燥方法对金银花的质量影响研究[J].食品科学.2006,27(11)：277-279.
[43]黄建立.银耳微波真空干燥机理及品质特性的研究[D].福州：福建农林大学,博士学位论文,2010.
[44]钟成义,肖宏儒,秦广明.枸杞子微波热风联合干燥技术研究[J].农业装备技术.2010,36(4)：10-13.
[45]曾绍校,梁静,郑宝东,等.不同干燥工艺对莲子品质的影响[J].农业工程学报.2007,23(5)：227-231.
[46]章斌,侯小桢.热风与微波联合干燥香蕉片的工艺研究[J].食品与机械.2010,26(2)：97-99.
[47]Khraisheh N A M, Cooper T J R, Magect R A. Shrinkage Characteristics of Potatoes Dehydrated Under Combined Microwave and Convective Air Conditions Drying[J]. Technology International.1997,15:1003-1022.
[48]Khraisheh M A, McMinn W A, Magee T R. Quality and Structural Changes in Starchy Foods During Microwave and Conventive Drying[J]. Food Research International.2004,37(5): 497-503.
[49]Emel Iraz Goksu, Gulum Sumnu, Ali Esin. Effect of Microwave on Fluidized Bed Drying of Macaroni Beads[J]. Journal of Food Engineering.2005, (66):463-468.
[50]周韵.热风微波耦合干燥特性研究[D].无锡：江南大学,硕十学位论文,2011.
[51]Vongpradubchai S, Rattanadecho P. Microwave and Hot Air Drying of Wood Using a Rectangular Waveguide[J]. Drying Technology.2011,29(4):451-460.
[52]Saxena Alok, Maity Tanushree, Raju P S, et al. Degradation Kinetics of Colour and Total Carotenoids in Jackfruit (Artocarpus Heterophyllus) Bulb Slices During Hot Air Drying[J]. Food and Bioprocess Technology.2012,5(2):672-679.
[53]Kumar Navneet, Sarkar B C, Sharma H K. Mathematical Modelling of Thin Layer Hot Air Drying of Carrot Pomace[J]. Journal of Food Science and Technology.2012,49(1):33-41.
[54]Hiranvarachat Bhudsawan, Devahastin Sakamon, Chiewchan Naphaporn. Effects of Acid Pretreatments on Some Physicochemical Properties of Carrot Undergoing Hot Air Drying[J]. Food and Bioproducts Processing.2011,89(2):116-127.
[55]Sturm Barbara, Hofacker Werner C, Hensel Oliver. Optimizing the Drying Parameters for Hot-Air-Dried Apples[J]. Drying Technology.2012,30(14):1570-1582.
[56]Akbudak Nuray, Akbudak Bulent. Effect of Vacuum, Microwave, and Convective Drying on Selected Parsley Quality[J]. International Journal of Food Properties.2013,16(1):205-215.
[57]Ganesapillai Magesh, Regupathi Lyyaswami, Murugesan Thanapalan. Characterization and Process Optimization of Microwave Drying of Plaster of Paris[J]. Drying Technology.2008, 26(12):1484-1496.
[58]Li Z Y, Wang R F, Kudra T. Uniformity Issue in Microwave Drying[J]. Drying Technology. 2011,29(6):652-660.
[59]Bal Lalit M, Kar Abhijit, Satya Santosh, et al. Kinetics of Colour Change of Bamboo Shoot Slices During Microwave Drying[J]. International Journal of Food Science and Technology. 2011,46(4):827-833.
[60]Motevali Ali, Minaei Saeid, Khoshtagaza Mohammad Hadi. Evaluation of Energy Consumption in Different Drying Methods[J]. Energy Conversion and Management.2011, 52(2):1192-1199.
[61]Dev S R S, Geetha P, Orsat V, et al. Effects of Microwave-assisted Hot Air Drying and Conventional Hot Air Drying on the Drying Kinetics, Color, Rehydration, and Volatiles of Moringa Oleifera[J]. Drying Technology.2011,29(12):1452-1458.
[62]Stephen A F, Charles R H. IntelliGin-A Ginning Revolution from Process Control Technology[J]. International Food and Agribusiness Management Review.1998,1(4): 555-566.
[63]Wiegand Christian, Herrmann Michael, Bachtier Sebastian, et al. APulsed THz Imaging System with a Line Focus and a Balanced 1-D Detection Scheme with Two Industrial CCD Line-scan Cameras[J]. Optics Express.2010,18(6):5595-5601.
[64]Honkanen Markus, Eloranta Hannu, Saarenrinne Pentti. Digital Imaging Measurements of Dense Multiphase Flows in Industrial Processes[J]. Flow Measurement and Instrumentation. 2010,21(1):25-32.
[65]Coates Colin, Campillo Chris. CCDs Lose Ground to New CMOS Sensors[J]. Laser Focus World.2011,47(3):40-45.
[66]Grace Carl R, Walder Jean-Pierre, Denes Peter, et al. Multiplexed Oversampling Digitizer in 65 nm CMOS for Column-Parallel CCD Readout[J]. IEEE Transactions on Nuclear Science. 2013,60(1):246-250.
[67]Aalsalem Mohammed Y, Khan Wazir Zada, Arshad Quratul Ain. A Low Cost Vision Based Hybrid Fiducial Mark Tracking Technique for Mobile Industrial Robots[J]. International Journal of Computer Science Issues.2012,9(44-2):151-156.
[68]王炜峰.数字图像处理技术在熟料窑监控系统中的应用[J].计算机测量与控制.2012,10(8)：511-513.
[69]柴阿丽.基于计算机视觉和光谱分析技术的蔬菜叶部病害诊断研究[D].北京：中国农业 科学院,博士学位论文,2011.
[70]吴兰兰.基于数字图像处理的玉米苗期田间杂草的识别研究[D].武汉：华中农业大学,博士学位论文,2010.
[71]Nagatani Keiji, Ikeda Ayako, Ishigami Genya, et al. Development of a Visual Odometry System for a Wheeled Robot on Loose Soil Using a Telecentric Camera[J]. Advanced Robotics. 2010,24(8-9):1149-1167.
[72]Ferria Kouider, Laouar Naamane, Bouaouadja Noureddine. Acousto-optic Method for Liquids Refractometry[J]. Optica Applicata.2011,41 (1):109-119.
[73]Florin Toadere, Mastorakis Nikos E. Simulation the Functionality of a Laser Pulse Image Acquisition System[J]. WSEAS Transactions on Circuits and System.2010,9(1):22-31.
[74]Lenk Felix, Vogel Mathias, Bley Thomas, et al. Automatic Image Recognition to Determine Morphological Development and Seconday Metabolite Accumulation in Hairy Root Networks[J]. Engineering in Life Sciences.2012,12(6):588-594.
[75]刘伟华.基于机器视觉的煤尘在线监测系统关键技术研究[D].济南：山东大学,博士学位论文,2011.
[76]魏新华.水果机器视觉自动分选机关键技术研究[D].南京：东南大学,博士学位论文,2008.
[77]李刚.基于图像工程的路面破损自动识别算法研究[D].西安：长安大学,博十学位论文,2010.
[78]王松磊.红枣自动分级分选机的研制[D].银川：宁夏大学,硕十学位论文,2009.
[79]Hong Byung-Joo, Park Chan-Oh, Seo Nam-Seok. A Real-time Compact Structured-light Based Range Sensing System[J]. Journal of Semiconductor Technology and Science.2012,12(2): 193-202.
[80]Bruno F, Bianco G, Muzzupappa M, et al. Experimentation of Structured Light and Stereo Vision for Underwater 3D Reconstruction[J].ISPRS Journal of Photogrammetry and Remote Sensing.2011,66(4):508-518.
[81]Barone Sandro, Paoli Alessandro, Razionale Armando Viviano. Three-dimensional Point Cloud Alignment Detecting Fiducial Markers by Structured Light Stereo Imaging[J]. Machine Vision and Applications.2012,23(2):217-229.
[82]Marsi Stefano, Ramponi Giovanni. A Flexible FPGA Implementation for Illuminance-reflectance Video Enhancement[J]. Journal of Real-time Image Processing.2013, 8(1):81-93.
[83]陆秋琰,陈坤杰.牛肉图像采集光照系统的设计与研究[J].农机化研究.2008,(6)：78-81.
[84]杨文柱,李道亮,魏新华,等.基于改进遗传算法的棉花异性纤维目标特征选择[J].农业机械学报.2010,41(4)：173-178.
[85]刘双喜,张馨,郑文秀,等.棉花异性纤维图像特征提取[J].农业机械学报,2010.41(3)：158-163.
[86]李国辉,苏真伟,夏心怡.基于不规则成像机器视觉的棉花白色异纤检测算法[J].农业机械学报.2010,41(5)：164-167.
[87]Nalpantidis Lazaros, Gasteratos A. Stereo Vision of Robotic Applications in the Presence of Non-ideal Lighting Conditions[J]. Liage and Vision Computing.2010,28(6):940-951.\
[88]Sun J, Smith M, Smith L, et al. Illumination Compensation for Nominally Planar Surface Recovery[J]. IET Computer Vision.2012,6(5):371-377.
[89]Muruganantham S, Jebarajan T. An Efficient Face Recognition System Based on the Hybridization of Pose Invariant and Illumination Process[J]. International Journal of Computer Science Issues.2012,9(44-1):228-237.
[90]Fouad Mohamed M, Dansereau Richard M, Whitehead Anthony D. Image Registration under Illumination Variations Using Region-based Confidence Weighted M-estimators[J]. IEEE Transactions on Imaged Processing.2012,21(3):1046-1060.
[91]Bong C W, Rajeswari M. Multiobjective Clustering With Metaheuristic: Current Trends and Methods in Image Segmentation[J]. IET Image Processing.2012,6(1):1-10.
[92]Acqua F D, Gamba P, Trianni G. Semi-automatic Choice of Acaledependent Features for Sateiilte SAR Image Classification[J]. Pattern Recognition Letters.2006,27(4):244-251.
[93]李洪艳,曹建荣,谈文婷,等.图像分割技术综述[J].山东建筑大学学报.2010,25(1)：85-89.
[94]Mayszko Dariusz, Stepaniuk Jarosaw. Adaptive Rough Entropy Clustering Algorithms in Image Segmentation[J]. Fundamenta Imformaticae.2010, 98(2-3):199-231.
[95]Marques Regic C Pinheiro, Medeiros Ftima N, Santos Nobre Juvencio. SAR Image Segmentation Based on Level Set Approach and GOA Model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2012,34(10):2046-2057.
[96]许凯,秦昆,黄伯和,等.基于云模型的图像区域分割方法[J].中国图象图形学报.2010,15(5)：757-763.
[97]谭洪波,侯志强,刘荣.基于人类视觉模型的区域生长图像分割[J].中国图象图形学报.2010,15(9)：1352-1356.
[98]Udupa J K, Saha P K, Lotufo R A. Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(11):1485-1500.
[99]龚永义,黄辉,于继明,等.基于熵的两区域图像分割[J].中国图象图形学报,2011,16(5)：754-760.
[100]Etemad S Ali, White Tony. An Ant-inspired Algorithm for Detection of Image Edge Features[J]. Applied Soft Computing Journal.2011,11(8):4883-4893.
[101]Kaur Amandeep, Singh Chandan. Sub-pixel Edge Detection Using Pseudo Zernike Moment[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition.2011, 4(2):107-118.
[102]Direkolu Cem, Nixon Mark S. Moving-edge Detection Via Heat Flow Analogy[J]. Pattern Recognition Letters.2011,32(2):270-279.
[103]Monaco James P, Madabhushi Anant. Class-specific Weighting for Markov Random Field Estimation: Application to Medical Image Segmentation[J]. Medical Image Analysis.2012, 16(8):1477-1489.
[104]Yousefi S, Kehtarnavaz N, Cao Y, et al. Bilateral Markov Mesh Random Field and Its Application to Image Restoration[J]. Journal of Visual Communication and Image Representation.2012,23(7):1051-1059.
[105]Abbasgholipour M, Omid M, Keyhani A, et al. Color Image Segmentation with Genetic Algorithm in a Raisin Sorting System Based on Machine Vision in Variable Conditions[J]. Expert Systems with Applications.2011,38(4):3671-3678.
[106]Ahmed Raghad Jawad. Genetic-based Multi Resolution Noisy Color Image Segmentation[J]. World Academy of Science, Engineering and Technology.2010,45,573-579.
[107]Chen Chieh-Li, Tai Chung-Li. Adaptive Fuzzy Color Segmentation with Neural Network for Road Detections[J]. Engineering Applications of Artificial Intelligence.2010,23(3):400-410.
[108]Baykan Nurdan Akhan, Yilmaz Nihat. Mineral Identification Using Color Spaces and Artifical Neural Networks[J]. Computers and Geosciences.2010,36(1):91-97.
[109]Nguyen Thi Nhat Anh, Cai Jianfei, Zhang Juyong, et al. Robust Interactive Image Segmentation Using Convex Active Contours[J]. IEEE Transactions on Image Processing. 2012,21(8):3734-3743.
[110]Kovacs Andrea, Sziranyi Tamas. Harris Function Based Active Contour External Force for Image Segmentation[J]. Pattern Recognition Letters.2012,33(9):1180-1187.
[111]Sklansky J, Image Segmentation and Fezture Extraction[J]. IEEE Transactions on Systems, Man, and Cybernetics.1978,8(5):237-247.
[112]Haralick R M. Statistical and Structural Approaches to Texture[J]. Proceedings of the IEEE. 1979,76(5):786-804.
[113]刘晓民.纹理研究及其应用综述[J].测控技术.2008,27(5)：4-9.
[114]孙君顶,马媛嫒.纹理特征研究综述[J].计算机系统应用.2010,19(6)：245-250.
[115]Haralick R M, Shanmugam K, Dinstein Ⅰ. Textural Features for Image Classification[J]. IEEE Transactions on System, Man, and Cybernetics.1973,3(6):610-621.
[116]Ulaby F T, Kouyate F, Brisco B, et al. Textural Information in SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing.1986,24(2):235-245.
[117]Baraldi A, Parmiggiani F. An Investigation of the Textual Characteristics Associated with Gray Level Co-ocurrenee Matrix Statistical Parameters[J]. IEEE Transactions on Geoscience and Remote Sensing.1995,33(2):293-304.
[118]薄华,马缚龙,焦李成.图像纹理的灰度共生矩阵计算问题的分析[J].电子学报.2006,34(1)：155-158.
[119]Clif Boykin J, Bechere Efrem, Meredith Jr, et al. Engineering and Ginning Cotton Genotype Differences in Fiber-seed Attachment Force[J]. Journal of Cotton Science.2012,16(3): 170-178.
[120]Barker G L. Analysis of Seed Cotton Cleaning Efficiency Using GINQUAL[J]. Applied Engineering in Agriculture.1991,7(6):667-672.
[121]Gillum M N, Armijo C B. Pima Seed Cotton Cleaning for Maximum Profit[J]. Transactions of the American Society of Agricultural Engineers.1997,40(3):513-518.
[122]Laird W, Columbus E P, Bragg C K. Seed Cotton Quality Separation by a Mechanical Cleaner[J]. Transactions of the American Society of Agricultural Engineers.1991,34(3): 718-726.
[123]Wanjura John D, Gamble Gary R, Holt Gregory A, et al. Influence of Grid Bar Shape on Field Cleaner Performance-field Testing[J]. Journal of Cotton Science.2012,16(4):255-267.
[124]Liu Gui, Yang Yu-Rong, Wang Ming-kui, et al. Comprehensive Characterization Model of Integrated Cotton Fiber Quality Index[J]. Journal of Donghua University(Engilsh Edition). 2011,28(4):379-383.
[125]Chaiba A, Abdessemed R, Bendaas M L. A Hybrid Intelligent Control Based Torque Tracking Approach for Doubly Fed Asynchronous Motor(DFAM) Drive[J]. Journal of Electrical Systems.8(3):262-272.
[126]Aleksendric Dragan, Jakovljevic Zivana, Cirovic Velimir. Intelligent Control of Braking Process[J]. Expert Systems with Applications.39(14):11758-11765.
[127]Farivar Faezeh, Shoorehdeli Mahdi Aliyari, Teshnehlab Mohammad. An Interdisciplinary Overview and Intelligent Control of Human Prosthetic Eye Movements System for the Emotional Support by a Huggable Pet-type Robot From a Biomechatronical Viewpoint[J]. Journal of the Franklin Institute.2012,349(7):2243-2267.
[128]Prakash Raghupathy, Anita Rajapalan. Robust Model Reference Adaptive Intelligent Control[J]. International Journal of Control, Automation and Systems.2012,10(2):396-406.
[129]Pontani Mauro, Conway Bruce A. Particle Swarm Optimization Applied to Implusive Orbtial Transfers[J]. Acta Astronautica.2012,74:141-155.
[130]Chalennchaiarbha Saksorn, Ongsakul Weerakorn. Elitist Multi-objective Particle Swarm Optimization with Fuzzy Multi-attribute Decision Making for Power Dispatch[J]. Electric Power Components and Systems.2012,40(14):1562-1585.
[131]Tewolde Girma S, Hanna Darrin M, Haskell Richard E. A Modular and Efficient Hardware Architecture for Particle Swarm Optimization Algorithm[J]. Microprocessors and Microsystems.2012,36(4):289-302.
[132]Shayeghi H, Shayanfar H A, Jalizadeh S, et al. Multi-machine Power System Stabilizers Design Using Chaotic Optimization Algorithm[J]. Energy Conversion and Management.2010, 51(7):1572-1580.
[133]Ahmadi Mohamadreza, Mojallali Hamed. Chaotic Invasive Weed Optimization Algorithm with Application to Parameter Estimation of Chaotic Systems[J]. Chaos, Solitions and Fractals. 2012,45(9-10):1108-1120.
[134]Okamoto Takashi, Hirata Hironori. Constrained Optimization Using a Multipoint Type Chaotic Lagrangian Method with a Coupling Structure[J]. Engineering Optimization.2013,45(3): 311-336.
[135]Tatsumi Keiji, Ibuki Takeru, Tanino Tetsuzo. A Chaotic Particle Swarm Optimization Exploiting a Virtual Quartic Objective Function Based on the Personal and Global Best Solutions[J]. Applied Mathematics and Computation.2013,219(17):8991-9011.
[136]Mukhopadhyay Sumona, Banerjee Santo. Global Optimization of an Optical Chaotic System by Chaotic Multi Swarm Particle Swarm Optimization[J]. Expert Systems with Applications. 2012,39(1):917-924.
[137]Alfi Alireza. Particle Swarm Optimization Algorithm with Dynamic Inertia Weight for Online Parameter Identification Applied to Lorenz Chaotic System[J]. International Journal of Innovative Computing, Information and Control.2012,8(2):1191-1203.
[138]Shirazi Masoud Jahromi, Vatankhah Ramin, Boroushaki Mehrdad, et al. Application of Particle Swarm Optimization in Chaos Synchronization in Noisy Environment in Presence of Unknown Parameter Uncertainty[J]. Communications in Nonlinear Science and Numerical Simulation. 2012,17(2):742-753.
[139]Firouzi Bahman Bahmani, Meymand Hamed Zeinoddini, Niknam Taher, et al. A Novel Multi-objective Chaotic Crazy PSO Algorithm for Optimal Operation Management of Distribution Network with Regard to Fuel Cell Power Plants[J]. International Journal of Innovative Computing, Information and Control.7(11):6395-6409.
[140]Sadeghpour Mehdi, Salarieh Hassan, Alasty Aria. Minimum Entropy Control of Chaos Via Online Particle Swarm Optimization Method[J]. Applied Mathematical Modelling.2012,36(8): 3931-3940.
[141]Safari A, Shayeghi H. Iteration Particle Swarm Optimization Procedure for Economic Load Dispatch with Generator Constraints[J]. Expert Systems with Applications.38(5):6043-6048.
[142]Coelho Leandro Dos Santos, Mariani Viviana Cocco. Firefly Algorithm Approach Based on Chaotic Tinkerbell Map Applied to Multivariable PID Controller Tuning[J], Computers and Mathematics with Applications.2012,64(8):2371-2382.
[143]Singh Manas Ranjan, Mahapatra S S. A Swarm Optimization Approach for Flexible Flow Shop Scheduling with Multiprocessor Tasks[J]. International Journal of Advanced Manufacturing Technology.2012,62(1-4):267-277.
[144]盛煜翔,潘海天,夏陆岳,等.混合混沌粒子群算法在苯与甲苯闪蒸过程优化中的应用[J].浙江工业大学学报,2010,38(3)：318-321.
[145]朱求红.锌冶炼除钴过程建模与智能优化方法研究及应用[D].长沙：中南大学,博士学位论文,2010
[146]Sheikhan Mansour, Shahnazi Reza, Garoucy Sahar. Synchronization of General Chaotic Systems Using Neural Controllers with Application to Secure Communication[J]. Neural Computing and Applications.2013,22(2):361-373.
[147]Abedini Mohammad, Vatankhah Ramin, Assadian Nima. Stabilizing Chaotic System on Periodic Orbits Using Multi-interval and Modem Optimal Control Strategies[J]. Communications in Nonlinear Science and Numerical Simulation.2012,17(10):3832-3842.
[148]Chaiba A, Abdessemed R, Bendaas M L. A Hybrid Intelligent Control Based Torque Tracking Approach for Doubly Fed Asynchronous Motor(DFAM) Drive[J]. Journal of Electrical Systems.8(3):262-272.
[149]Aleksendric Dragan, Jakovljevic Zivana, Cirovic Velimir. Intelligent Control of Braking Process[J]. Expert Systems with Applications.39(14):11758-11765.
[150]Farivar Faezeh, Shoorehdeli Mahdi Aliyari, Teshnehlab Mohammad. An Interdisciplinary Overview and Intelligent Control of Human Prosthetic Eye Movements System for the Emotional Support by a Huggable Pet-type Robot From a Biomechatronical Viewpoint[J]. Journal of the Franklin Institute.2012,349(7):2243-2267.
[151]Prakash Raghupathy, Anita Rajapalan. Robust Model Reference Adaptive Intelligent Control[J]. International Journal of Control, Automation and Systems.2012,10(2):396-406.