零/低航速减摇鳍升力模型及系统控制策略研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
船舶在海上行驶过程中由于受到海浪、海风和海流等各种海洋扰动的作用,不可避免的要产生六自由度运动,即横摇、纵摇、艏摇、横荡、垂荡和纵荡运动。在同种海洋干扰环境下,船舶的横摇运动较为剧烈,严重影响船载设备的正常运行、船舶乘员的舒适度和船载货物的安全性。传统减摇鳍是目前最常用且应用最成功的船舶主动式减横摇装置,减摇效果可达90%以上由工作原理所限,只有当船舶处于中高航速时,传统减摇鳍才可以有效地减摇,在低航速或零航速情况下,传统减摇鳍几乎不能进行减摇。现有的减摇设备中,只有减摇水舱的减摇效果不受航速影响,但是减摇水舱体积较大,而且只是在谐摇频率的附近才能取得较好的减摇效果,在某些情况下甚至会出现增摇的现象。为实现船舶在全航速下的有效减摇,有些船舶采用同时安装减摇鳍和减摇水舱两种减摇设备。采用两套减摇设备虽然能取得良好的减摇效果但经济效益欠佳。本文以设计一种具有零航速、低航速和中高航速种工作模式的全航速减摇鳍系统为前提,通过工作模式的转变实现在减摇鳍控制下船舶全航速航行状态下的有效减摇。中高航速减摇鳍系统已经相当成熟,本文将重点从零/低航速减摇鳍升力模型和系统控制策略两方面对零/低航速减摇鳍系统进行研究。
     本课题来源于国家自然科学基金项目“零速下船舶仿生减摇鳍升力机理的研究(50575048)”和“基于变形仿生减摇鳍的水面机器人的减摇机理研究(50879012)”。主要研究零/低航速下减摇鳍基本工作模式、基于此工作模式的零/低航速减摇鳍升力产生机理,以及零/低航速下减摇鳍系统控制策略。
     论文首先从零/低航速减摇鳍系统安装方式和工作原理的角度对零/低航速减摇鳍系统进行了系统的阐述。介绍了基于Weis-Fogh机构零航速减摇鳍系统、基于纵向拍动工作模式零航速减摇鳍系统、基于变形鳍技术零航速减摇鳍系统和基于纵向拍动工作模式低航速减摇鳍系统四种减摇鳍系统的安装方式和工作原理。
     分析并得到零/低航速下减摇鳍升力模型是零/低航速减摇鳍系统设计的关键,从系统实现和减摇效果的角度出发,本文将重点对基于纵向拍动工作模式零航速减摇鳍系统、基于变形鳍技术零航速减摇鳍系统和基于纵向拍动工作模式低航速减摇鳍系统进行研究。依据流体力学理论对基于纵向拍动工作模式的固定翼型零航速减摇鳍升力模型、基于变形鳍技术零航速减摇鳍和低航速减摇鳍升力模型进行分析。针对减摇鳍的实际工作环境,我们将减摇鳍在流体中转动时产生的升力分为理想流体环境和非理想流体环境两部分分别进行分析。在理想流体环境下我们通过一系列保角变换、运动变换求得鳍面在理想流体中转动时引起的流场复势,然后采用伯拉休斯定理,分析得到理想流体中鳍面转动时产生升力数学模型。在非理想流体环境下,由于流体存在粘性,因此鳍面在转动的过程中势必会在鳍面产生旋涡,旋涡会在鳍面两侧产生压力差,产生阻碍鳍面转动的阻力,从而影响鳍面转动时产生的升力。由于鳍面在转动时引起的旋涡变化复杂,从工程应用的角度出发,在对鳍面旋涡作用力进行分析时,我们忽略鳍面厚度,采用相对简化的旋涡模型分析得到鳍面在非理想流体中转动时产生的升力数学模型。
     由于船舶的航行情况及装载情况并非固定不变,这就使得船舶横摇运动模型存在一定的时变性。通过分析我们得到的零/低航速减摇鳍升力模型都存在一定的非线性特性,这就使得零/低航速减摇鳍系统具有一定的非线性和时变特性。由于支持向量机具有良好的小样本辨识能力,因此我们采用基于支持向量机在线辨识的前馈逆控制器设计方法来解决系统的非线性特性和时变特性。支持向量机辨识是一种无误差反馈辨识,因此支持向量机对系统的辨识有可能存在一定的不足,这将影响系统的稳定性和稳态性,同时当系统模型突然发生变化时支持向量机也需要一定的时间对系统进行辨识。为解决因支持向量机辨识不足和系统模型突变引起的系统控制不足的问题,我们引入了基于支持向量机和模糊控制相结合的复合控制策略,通过模糊控制器保证支持向量机辨识过程中系统的稳定性和稳态性。定义一个协调因子,根据系统运行效果调整两种控制策略所占的比重,根据协调因子的数值决定支持向量机是否需要重新对系统进行辨识和系统辨识是否充分。该控制结构既具有支持向量机逆控制设计简单、稳态精度高的优点,同时结合模糊控制响应快速,抗干扰能力强,鲁棒性好的优势,既保证了系统的稳定性又保证了系统的稳态性。
     采用本文分析得到的零/低航速减摇鳍升力模型搭建船舶零/低航速减摇鳍系统模型,依据本文提出的控制策略对系统进行控制器设计,并对系统进行仿真。仿真显示三种减摇鳍系统均取得了良好的减摇效果,证明了本文设计的零/低航速减摇鳍系统的有效性。由于零航速减摇鳍升力容量有限,随着海情的增加两种零航速减摇鳍系统减摇效果有所下降,与零航速减摇鳍相比低航速减摇鳍具有较大的升力容量,其减摇效果受海情影响不大。
When the ship sails on the sea, it will move in six degree of freedom for the disturbance of ocean wave,sea breeze,ocean current. The rolling movement is the most serious under the same ocean disturbance. Rolling movement has a serious effect on working order of the equipment in the ship, the cosiness of passenger and the security of goods on the ship. Fin stabilizers are the most successful and most widely used active equipments for ship anti-roll.90 percent of roll motion can be eliminated by using fin stabilizers. As the work principle of fin stabilizer, it can effectively reduce the rolling movement when the ship sails at a high speed and it can not reduce the rolling movement when the ship sails at a low speed or zero speed. Among existing equipments, only the effectiveness of ship anti-roll tank is not affected by the speed of ship. But the volume of tank is usually too large. Ship anti-roll tank can work well when disturbance resonance frequency is close to that of ship. If the resonance frequency doesn't match, the roll motion of ship might be strengthened. In order to reduce ship roll movement at all speed, some ships adopt ship anti-roll tank and fin stabilizer together. Though it can get good perfection, it is not a economical method. In this dissertation we dedicate to research a fin stabilizer system with three kinds of working method which are zero speed,low speed and high speed working method. Fin stabilizer system at high speed has been well developed. We will mainly research on zero and low speed fin stabilizer system.
     The dissertation is based on project supported by national science foundation of China:Research on lift theory of ship bionic roll stabilization at zero speed(50575048) and Research on antiroll of underwater vehicle near surface based on Transmutative Fin (50879012). It mainly researches on the working mode of zero and low speed fin stabilizer,the lift model of zero and low speed fin stabilizer and the control strategy of zero and low speed fin stabilizer system.
     The working mode and the fixing fashion of zero and low speed fin stabilizer are firstly expatiate. Then four kinds of zero and low speed fin stabilizer system which are zero speed fin stabilizer system with Weis-Fogh mechanism, zero speed fin stabilizer system with lengthways movement, zero speed fin Stabilizer system with transmutative fin and low speed fin Stabilizer system with lengthways movement.
     The key factor in designing of zero and low speed fin stabilizer system is to analyse and get the lift model of fin at zero and low speed. We will mainly research on zero speed fin stabilizer system with lengthways movement, zero speed fin Stabilizer system with transmutative fin and low speed fin stabilizer with lengthways movement for their predominance. We analyse the lift generated on the fin in ideal hydro environment and nonideal hydro environment. We get the lift model in ideal environment with conformal representation theory,movement transform theory and Blasius theorem. As glutinosity of the hydro in nonideal hydro environment, it will generate vortices on fins when fins move in nonideal hydro environment. The vortices will generate press distinction on two sides of the fin and it is one parts of lift generate on the fin. We ignore the thickness of fin for the complexity of vortices and adopt a applied method to analyse the lift generated by vortices.
     The model of ship rolling movement is variational for the change of sail and loading of the ship. All the three kinds of lift model are nonlinear. So the zero and low speed fin stabilizer system are a serious variational and nonlinear system. As SVM(support vector machine) has a good identification ability, we adopt a inverse control method based on SVM to solve the nonlinearity and time varying of zero and low speed fin stabilizer system. The identification of SVM is without feedback-error, So the identification of SVM to zero and low speed fin stabilizer system maybe has some insufficient and it will affect the stability and the stationarity of the system. SVM also need some time to identify the system when the system has a sudden change. We adopt a compound control strategy based on SVM and GAFC(fuzzy controller based on genetic algorithm) for the under-control of the system that is induced by the sudden change of the system and under-identification of SVM. The fuzzy controller will make the system has a good stability and the stationarity when SVM is identifying zero and low speed fin stabilizer system. Define a coordination factor, To adjust the proportion of the two kinds of control strategies and to determine whether to identify the system once again or the identification is sufficient based on the coordination factor. This control structure can make the system has a good stability characteristic and static accuracy.
     To construct zero and low speed fin stabilizer system with the lift model that we get and design controller with complex control strategy that we introduce. Simulation results show that all the fin stabilizer system get a good reduction of ship rolling angle and it prove the efficiency of zero and low speed fin stabilizer system. As zero fin stabilizer's lift capacity is not very large,the anti-roll effect will decrease when the oceanic condition become more serious.As low speed fin stabilizer has a large lift capacity,the anti-roll effect of low speed fin stabilizer system is not severely affected by the oceanic condition.
引文
[1]金鸿章,姚绪梁.船舶控制原理.哈尔滨:哈尔滨工程大学出版社,2001
    [2]金鸿章,李国斌.船舶特种装置控制系统.北京:国防工业出版社,1995
    [3]王献孚,熊鳌魁.高等流体力学.武汉:华中科技大学出版社,2003
    [4]Bass D W. Roll Stabilization for Small Fishing Vessels Using Paravanes and Anti-rolling Tanks. Marine Technology. Vol.35, Uo.2,1998(4):74-84P
    [5]綦志刚.船舶零航速减摇鳍升力机理及系统模型研究.哈尔滨工程大学博士学位论文,2008
    [6]罗延明.零航速减摇鳍及其电动伺服系统研究.哈尔滨工程大学博士学位论文,2007
    [7]张晓飞.船舶零航速减摇鳍建模及控制策略研究.哈尔滨工程大学博士学位论文,2008
    [8]金鸿章,罗延明,綦志刚,杨洲城.基于Weis-Fogh机构的零航速减摇鳍升力特性研究.哈尔滨工程大学学报.2007(7):762-767页
    [9]J. Ooms. The use of roll stabilizer fins at zero speed. Quantum Controls BV, Nuth, Holland.2002
    [10]R.P. Dallinga. Roll stabilization at anchor:Hydrodynamic aspects of the comparison of anti-roll tanks and fins. Project 2002, Amsterdam.2002
    [11]H.M. van Wieringen. Design considerations on at anchor stabilizing system. Project 2002, Amsterdam.2002
    [12]R.P. Dallinga. Roll stabilization of motor yachts:use of fin stabilizers in anchored conditions. Project 1999, Amsterdam.1999.
    [13]Thomas W. Treakle III, Dean T. Mook, and Stergios I. Liapis et al. A time-domain method to evaluate the use of moving weights to reduce the roll motion of a ship. Ocean Engineering.2000(27):1321-1343P
    [14]缪国平.关于舵在尾斜浪和横浪中横摇衰减效果的理论研究.中国造船.1982(1):35-43页
    [15]缪国平,朱文蔚等.舵减摇的实验研究.中国造船,1991(1):39-46页
    [16]葛德宏,高启孝,陈永冰,李安.船舶舵阻摇技术的研究现状及展望.舰船科学技术.2007(4):22-26页
    [17]Michiko Usui,Andrew Simpson,Suzanne Smith, and Jamey. Development and flight testing of a UAV with inflatable rigidizable wings. AIAA 2004-1373P
    [18]A.Simpson,M.Usui,S.Simth,and J.Jacob. Aeromechanics of inflatable wings. AIAA 34th Fluid Dynamic Conference,Portland,OR,June,2004
    [19]J.D.Kearns,M.Usui,S.W.Simth. Development of UV-curable inflatable wings for low-density flight application. AIAA SDM Gossamer Spacecraft forum,Palm Spring. CA, April 2004
    [20]赖志昌.U型减摇水舱及试验摇摆台实验研究.哈尔滨工程大学博士学位论文.2002
    [21]樊社军.可控被动式减摇水舱的理论与设计.上海交通大学硕士论文.1990
    [22]寺尾裕等.新型被动可控式减摇水舱的系统模拟和海上试验.关西造船协会志.1993(219):75-88页
    [23]陶尧森等.减摇水舱的研究与进展.中国造船增刊-船舶耐波性专辑.1991:313-323页
    [24]金鸿章,綦志刚,罗延明,巩晋.基于Weis-Fogh机构的零航速减摇鳍升力模型的研究.系统仿真学报.2007(19):4079-4081页
    [25]李妍妍.SVM理论极其在船舶机炉协调智能控制中的应用.哈尔滨工程大学博士学位论文.2007
    [26]Jian-xiong Dong, Adam Krzy_zak, Ching Y. Suen. An improved handwritten Chinese character recognition system using support vector machine. Pattern Recognition Letters,2005,26:1849-1856P
    [27]陈爱军,宋执环等.基于矢量基学习的最小二乘支持向量机建模,控制理论与应用.2007(2):1-5页
    [28]宋夫华,郑恩辉.基于支持向量机的非线性内模解耦控制,控制理论与应用.2008(6):1067-1042页
    [29]李洪兴.变论域自适应模糊控制器.中国科学(E)辑.1999,29(1):10-20页
    [30]段萍,张建畅.基于模糊遗传算法的移动机器人墙跟踪控制策略.控制理 论与应用.2006(3):416-420页
    [31]杨燕.基于遗传算法模糊控制器研究,安徽理工大学硕士学位论文.2007
    [32]修春波,刘向东,张宇河.混沌优化与模糊控制在混沌控制中的应用.控制理论与应用.2005(1):63-67页
    [33]杨延西等.基于模糊遗传算法的二自由度PID控制器优化设计.仪器仪表学报.2006(8):868-872页
    [34]苏伟生.基于遗传算法的航空发动机PI控制器参数优化方法.航空动力学报.2005(6):1078-1082页
    [35]Michael S. Triantafyllou, Alexandra H. Techet, Franz S. Hover. Review of experimental working in biomimetic foils. IEEE Journal of Oceanic Engineering.2004(29):585-594P
    [36]王华羽.NJ系列减摇鳍技术手册.哈尔滨:哈尔滨工程大学出版社,1997
    [37]王献孚.船用翼理论.北京:国防工业出版社,1998
    [38]Weis-Fogh,T. Quik estimates of flight fibness in hovering animals including novel mechanism for lift production. Exp Biol.1973(59):169-230P
    [39]Lightill,M J. The Weis-Fogh Mechanism of Lift Generation. Fluid Mech.1973(60):1-17P
    [40]章社生,王献孚,吴秀恒.Weis-Fogh机构流体力学.北京:国防工业出版社,2002
    [41]Bandyopadhyay P R. Low speed maneuvering hydrodynamics of fish and small underwater vehicls. Jour of Fluids Engineering.1997(1):136-144P
    [42]Ellington C P, Van der Berg, Willmott C, A P, Thomas A L R. Leading-edge vorticses in insect flight, Nature.1996(3):626-63 OP
    [43]Maxworthy T. Experiments on the Weis-Fogh Mechanism of Lift Generation by Insects in hovering Flight. Dynamics of the Fling, J Fluid Mech.1979(93):47-63P
    [44]Carozza M, Rampone S. Towards an incremental SVM for regression. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy,2000,6:405-410P
    [45]Krebel U H. Pairwise classification and support vector machines. Advances in Kernel Methods:Support Vector Learning, Cambridge, MA:MIT Press,1999,15:255-268P
    [46]Platt J, Cristianini N, Shawe-Taylor J.Large margin DAGs for multiclass classification. Advances in Neural Information Processing System, MIT Press,2000:547-553P
    [47]Mark Armstrong, Maria Sotoj. Quantum marine engineering introduces extendable fin stabilizers-XTTM fin for superyachts. Project 2006, Florida.2006.
    [48]Yadykin Y, Tenetov V, Levin D. The added mass of a flexible plate oscillating in a fluid. Journal of Fluids and Structures.2003(17):115-123P
    [49]綦志刚.减摇鳍在零航速下升力的研究及仿真.哈尔滨工程大学硕士学位论文.2005
    [50]Zhang Shesheng, Wang Xianfu. A numerical model of the Weis-Fogh mechanism with aseparation vortex. Jour of Hydro.1994(4):97-102P
    [51]Luara A. Miller, Charles S. Peskin. A computational fluid dynamics of 'clap and fling'in the smallest insects. J Exp. Biol.. 2005(208):195-212P
    [52]章社生,吴秀恒,王献孚.叶栅复合Weis-Fogh翼推进装置及应用研究.中国造船.1998:42-54页
    [53]Tsutahara M, Kimura T, Ro k. Ship's propulsion mechanism of Two-Stage Weis-Fogh type. ASME Jour of Fluid Engi.1994(2):278-286P
    [54]Tsutahara M, Kimura T. An application of the Weis-Fogh Mchaninsm to Ship Propulsion. Journal of Fluid Engineering.1987(2):107-113P
    [55]苏玉民,黄胜,庞永杰,徐玉如,吴强.仿鱼尾潜器推进系统的水动力分析.海洋工程.2002(2):54-59页
    [56]俞经虎,竺长安,朱家祥,钟小强,程刚,陈宏,张屹.仿生机器鱼尾鳍的动力学研究.系统仿真学报.2005(4):947-953页
    [57]张晓庆,王志东,张振山.二维摆动水翼仿生推进水动力性能研究.水动力学研究与进展.2006(5):632-639页
    [58]刘贵春.多仿生机器鱼协调控制的研究和设计.北方工业大学硕士学位论文.2007
    [59]刘贵春.仿生机器鱼巡游和机动的运动机理研究.中国科学技术大学博士学位论文.2006
    [60]蒋小勤,杜德锋,周骏.行波推进仿生机器鱼.海军工程大学学报.2007(10):1-5页
    [61]吴介之,马晖扬,周明德.涡动力学引论.北京:高等教育出版社,1993
    [62]J.W.戴莱,D.R.F.哈里曼.流体动力学.北京:人民教育出版社,1981
    [63]王福军.计算流体力学分析-CFD软件原理与应用.北京:清华大学出版社,2004,9:24-91页
    [64]FLUENT Inc. GAMBIT User Defined Function Manual. Fluent Inc.2003
    [65]FLUENT Inc. FLUENT User's Guide. Fluent Inc.2003
    [66]王田苗,张丽,黄毓瑜,梁建宏.仿生机器鱼艏向摆动动力学仿真及分析.计算机仿真.2006(2):133-136页
    [67]赵攀峰,刘春阳,祝隆伟,陈小燕,杨基明.二维平板翼拍翼运动的涡流场显示.中国科学技术大学学报.2005(4):441-446页
    [68]倪汉根,陈霞.平面旋涡(中心型奇点)水利特性的探讨.水利学报.1998(11):50-56页
    [69]景思睿,张鸣远.流体力学.西安:西安交通大学出版社,2001
    [70]章社生,吴秀恒,王献孚.复合Weis-Fogh翼振动机构流体动力.第十届全国水动力学会议论文.1996
    [71]朱仁庆,杨松林,杨大明.实验流体力学.北京:国防工业出版社,2005
    [72]Jin Hongzhang, Zhang Xiaofei, Luo Yanming. Application of Generalized Predictive Control Algorithm on Fin Stabilizer at Zero Speed[C].Chengdu,China: 3rd International Workshop on Signal Design and Its Applications in Communications.2007:356-360P
    [73]陈放.鳍水动力应用及鳍和水舱综合减摇系统研究.哈尔滨工程大学博士学位论文.2005
    [74]Jin Hongzhang, Zhang Xiaofei, Luo Yanming. Fuzzy Control of Fin Stabilizer at Zero Speed Based on Improved Genetic Algorithm[C]. Chengdu,China:2007 International Conference on Intelligent Systems and Knowledge Engineering.2007:518-523P
    [75]高永卫.实验流体力学基础.西安:西北工业大学出版社,2002
    [76]夏国泽.船舶流体力学.武汉:华中科技大学出版社,2003
    [77]Myong Hwan Sohn, Jo Won Chang. Flow visualization and aerodynamic load calculation of three types of clap-fling motions in a Weis-Fogh mechanism. Aerospace Science and Technology.2007 (11):119-129P
    [78]Lehmann F 0. The mechanisms of lift enhancement in insect flight. Naturwissenschaften.2004(91):101-122P
    [79]金鸿章,谷云彪,汪滨琦.减摇鳍变参数PID控制器的设计.船舶工程.1994(4):55-59页
    [80]金鸿章.减摇鳍PID调节器参数优化及其仿真.船工科技.1984(3):24-27页
    [81]金鸿章.减摇鳍参数最优控制器及其设计.中国造船.1992(4):30-33页
    [82]叶瑰昀,罗耀华,金鸿章.减摇鳍模糊参数自整定PID控制器设计及仿真研究.船舶工程.2002(3):39-42页
    [83]金鸿章,王科俊等编著.智能技术在船舶减摇鳍系统中的应用.第一版.北京:国防工业出版社,2003
    [84]赵登福,王蒙,张讲社等.基于支撑向量机的短期负荷预测.中国电机工程学报.2002,22(4):26-30页
    [85]李丽娜,候朝桢.基于支持向量机(SVM)的工业过程辨识.北京理工大学学报.2003,23(5):569-580页
    [86]刘航等.基于改进单亲遗传算法的被动传感器数据关联.控制与决策.2008(4):464-472页
    [87]张军英,许进等.遗传交叉运算的可达性研究.自动化学报.2002,28(1):120-125页
    [88]Laskov P. Feasible direction decomposition algorithms for training support vector machines. Machine Learning,2002,46(1):315-349P
    [89]韩华,罗安等.一种基于遗传算法的非线性PID控制器.控制与决策.2005(4):448-454页
    [90]杜贞斌,胡寿松.一类复杂非线性系统的模糊控制.控制理论与应用.2008(4):780-782页
    [91]Alistair Shilton, M. Palaniswami. Incremental Training of Support Vector Machines. IEEE Trans on Neural networks,2005,16(1):114-131P
    [92]De Bastos, De campos. A fast training algorithm for unbiased proximal SVM. In Proceedings International Conference on Acoustics Speech and Signal Processing, Philadelphia,2005:245-248P
    [93]李洪兴.非线性系统的变论域稳定性自适应控制.中国科学(E)辑.2002,32(2):211-223页
    [94]张浩然,韩正之,李昌刚.基于支持向量机的未知非线性系统辨识与控制.上海交通大学学报.2003,37(6):927-930页
    [95]Sun Zonghai, Sun Youxian. Optimal control by weighted least squares generalized support vector machines. In Proceedings of the American Control Conference, Denver, Colorado,2003:5323-5328P
    [96]Johan A. K. Suykens. Nonlinear Modeling and support vector machines. In Proceedings of International Conference on IEEE Instrumentation and Measurement Technology, Budapest, Hungary,2001:287-294P
    [97]Jongcheol Kim, Sangchul Won. New fuzzy inference system using a support vector machine. In Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, Nevada USA,2002:1349-1354P
    [98]Ruey-Feng Chang, Wen-Jie Wu, Woo Kyung Moon et al. Support Vector Machines for Diagnosis of Breast Tumors on US Images. Academic Radiology,2003,10(2):189-197P
    [99]Reyna R. A., Hernandez N., Esteve D. et al. Segmenting images with support vector machines. In Proceedings of 2000 International Conference on Image Processing. Vancouver, BC, Canada.2000,1(1):820-823P
    [100]Valentini A., Zhang Hongjiang. Automatic image orientation detection. IEEE Trans on Image Processing,2002,11(7):746-755P
    [101]Guo Guo-Dong, Jain A. K., Ma Wei-Ying et al. Learning similarity measure for natural image retrieval with relevance feedback. IEEE Trans on Neural Networks,2002,13(4):811-820P
    [102]Brown M, Lewis H. G., Gunn S. R.. Linear spectral mixture models and support vector machines for remote sensing. IEEE Trans on Geosciences and Remote Sensing,2000,38(5):2346-2360P
    [103]Shutao Li, James T., Yaonan Wang. Texture classication using the support vector machines. Pattern Recognition,2003,36:2883-2893P
    [104]Yongmin Li, Shaogang Gong, Jamie Sherrah, Heather Liddell. Support vector machine based multi-view face detection and recognition. Image and Vision Computing,2004,22:413-427P
    [105]Jian-xiong Dong, Adam Krzy_zak, Ching Y. Suen. An improved handwritten Chinese character recognition system using support vector machine. Pattern Recognition Letters,2005,26:1849-1856P
    [106]Valentini and Giorgio. Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles. Artificial Intelligence in Medicine,2002,26(3):281-304P
    [107]Cai Yu-dong, Liu Xiao-jun, Xu Xue-biao, et al. Prediction of protein structural classes by support vector machines. Computers and Chemistry, 2002,26(3):293-296P
    [108]Bao L., Sun Z.. Identifying genes related to drug anticancer mechanisms using support vector machine. FFBS letters,2002,521(1):109-114P
    [109]王琛,王仕成.基于遗传算法的PID参数整定及仿真.计算机仿真.2005(10):110-114页
    [110]谢勤岚,陈红.基于遗传算法的PID控制器优化设计.光学与光电技术.2003(3):37-40页
    [111]陈向阳,苗广祥.基于自适应遗传算法的PID参数优化仿真研究.自动化与仪表.2005(1):30-32页
    [112]李萌.基于自适应遗传算法的过热气温PID参数优化控制仿真研究,中国电机工程学报.2002(8):145-149页
    [113]冯永宝,丘泰.基于改进遗传算法的微波吸收材料优化设计,南京航空航天大学学报.2005(4):232-235页
    [114]王顺晃,舒迪前.智能控制系统及其应用.北京:机械工业出版社,2005
    [115]黎钧琪,石国桢.遗传算法交叉率与变异率的研究.武汉理工大学学报(交通科学与工程版).2003(1):97-99页
    [116]李培志,樊丁.基于实数编码的改进遗传算法研究.宇航计测技术.2008(2):54-57页
    [117]张文修,梁怡.遗传算法的数学基础.西安:西安交通大学出版社,2000
    [118]郭书杰,梁旭,赵敏来.基于改进遗传算法的旅行商问题的求解.大连交通大学学报.2008(4):64-66页
    [119]张芳斌,张丽静等.改进遗传算法在控制系统参数优化中的应用.仪器仪表学报.2003(4):604-605页
    [120]王雪松,田西兰,程玉虎等.基于协同最小二乘支持向量机的Q学习.自动化学报.2009(2):214-219页
    [121]贾华丁,游志胜等.采用二重扰动机制的支持向量机的集成训练算法.控制与决策.2008(7):828-832页
    [122]于华男.基于遗传算法的水下机器人模糊控制器优化设计.哈尔滨工程大学学报.2002,23(5):12-15页