蚁群算法在飞行模拟器平台中若干应用问题的研究
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摘要
二十一世纪以来,各国空军的发展代表了军队建设的现代化进程,而飞行模拟器为空军的训练机制提供了完整的技术支持,它的引入具有节省训练经费,保障飞行员安全,轻松完成复杂特情训练等优势,因此我国在部队现代化进程中也对飞行模拟器开展了大量的立项研究,目前来讲飞行模拟器由平台系统、计算机系统、视景系统、电子控制系统以及控制台系统组成,其中平台系统、计算机系统以及视景系统随着多年实践经验的积累已经日趋稳定。目前随着对模拟器模拟实战能力要求的提高,如何利用飞行控制台提供集团作战模拟是亟待解决的问题之一,同时大量集成电路的引入也为模拟器的维护增加了困难,针对大规模集成电路的故障诊断一直是国际上研究的热点问题,本文将以上述几点为依据展开研究。
蚁群算法源于人们对生物界蚁群觅食行为的观察和模拟,相较于传统的优化方法,蚁群算法在解决各类优化问题时具有很强的适应性和鲁棒性,适用于各类组合优化问题。蚁群算法在使用过程中参数设置简单,易于实现,因此在众多科学研究以及生产制造领域得到了广泛的应用,本文以蚁群算法为基础同时结合遗传算法和模拟退火算法两种仿生算法,对飞行模拟器平台中的多目标数据关联问题,数字电路测试集生成以及测试矢量集合优化问题进行了一定程度的研究,并且通过相关实验将本文算法与现有的一些算法进行了对比,实验结果表明,本文提出的几种算法能有效的提高蚁群算法的收敛速度,克服局部极值的出现,算法在迭代次数和执行效率上都体现出了很大的优势,本文的具体研究内容如下:
1.以多传感器多目标数据关联问题为研究对象,提出了一种基于蚁群-遗传算法的AC-GADA算法,该算法结合了遗传算法的染色体交叉变异思想,为蚁群中的个体赋予了单独的染色体,同时对蚁群算法的信息素模型进行了改进,在每一代种群寻优之后,利用交叉变异机制对蚁群进行重新构造,同时结合了控制种群适应度的方法,进一步做到了个体差异化,有效地避免了局部极值的出现,在AC-GADA算法中,信息素强度不仅与途经该路径的蚂蚁数目相关,同时还受蚂蚁自身的信息素编码影响,因此AC-GADA算法具有更强的随机搜索能力,通过大量的实验表明AC-GADA算法在收敛速度、关联的准确率和成功率上都有明显的优势。
2.针对大规模集成电路的故障诊断问题,提出了AC-GATSG算法,该算法首先将大规模集成电路的关联矩阵转化为图结构,从而利用蚁群算法解决图着色问题上的优势,对电路测试集的自动生成问题进行探索实现,在处理图节点着色问题时同样引入了交叉变异机制,在每代的遍历过程出现最优解时对蚁群个体进行染色重构,同时更新信息素,并将阈值引入信息素扩散模型中,利用阈值限定了交叉变异过程中产生突变个体的几率,降低了算法执行过程中由一个局部极值陷入另一个局部极值的可能。在对比实验中选取了标准组合电路以及典型的飞行模拟器电路单元为对象,通过与一些经典算法以及以仿生学算法为基础的文献算法对比,验证了AC-GATSG算法的有效性,AC-GATSG算法能够满足模拟器生产实践中对基础电路单元的测试集生成需求,并且具有广阔的应用前景。
3.自动生成的大规模集成电路测试集往往存在冗余问题,在进行标准化测试时,这些冗余矢量会造成大量的资源浪费,影响生产和研发的效率,针对上述问题本文提出了AC-SATSO算法对电路测试集进行优化,AC-SATSO算法首先对故障节点与测试矢量节点进行分层建模,并通过故障入度和测试矢量出度对双层节点进行标识,AC-SATSO算法利用模拟退火算法的扰动机制加速种群的收敛速度,并通过Boltzmann机制判断是否接纳新的种群,这种寻优策略在算法执行初期可以避免由于缺乏信息素而导致的寻优速度慢的问题,后期则有效的限制了局部极值的出现并且同时保留了优良的数据集,随着迭代次数的增加,模拟退火算法保留优秀结果的概率也大大增加,同时也加速了算法的收敛。并且针对生产实践中的测试环节本文提出了最优完备测试集的概念,即所需系统开销最少的完全测试集,通过不同规模的测试集优化对比试验证明,AC-SATSO算法在求解质量和稳定性上都优于文献算法。
近年来,蚁群算法等仿生算法的研究受到了国内外学者的关注,并且涌现出了大量的改进算法和新的领域应用,本文以蚁群算法为基础,结合遗传算法、模拟退火算法在飞行模拟器平台中展开了一些研究,提出了几种相互结合的优化算法,在蚁群算法的改进以及更丰富领域的应用上,其研究具有一定的实际意义。
In twenty-first century, the development of national Air Force represent the level of modernization of the military construction, and the flight simulator provides the complete technical support for Air Force training system, there are a lot of advantages of flight simulator such as saving training fund, protecting pilots’lives and performing special and complex training, and we have carried out a large number of research projects of flight simulator. The flight simulator is composed by platform system, computer system, visualization system, electronic controling system and console system. With the years of experiences, the platform system, computer system and visualization system have become increasingly stable. The group operation in complex situation is a very urgent problem that needs to be solved due to the requirement of higher combat abilities of flight simulator. A increasing number of integrated circuits also increase the difficulty of maintain flight simulator, and fault diagnosis of large-scale integrated circuits has became an international issue.
Ant colony algorithm originated from the observation and simulation of the ants’behaviors. Compared with the traditional optimization methods, AC is widely applied in scientific research and industrial production in virtue of their higher adaptability, robustness and parallel processing capability. This paper ccommence the study in multi-target data association, digital circuit test set generation and test set optimization with AC combined with genetic algorithm and simulated annealing algorithm. The result of experiments has shown that AC-GADA, AC-GATSG and AC-SATSO can effectively improve the convergence speed of the ant colony algorithm, and overcome the emergence of local extremum. The main contents of this thesis are as follows:
For multi-sensor multi-target data association problem, this paper presents an AC-GADA algorithm which combines ant colony algorithm with genetic algorithm. This algorithm designed difference pheromone for each ant and improves global pheromone increment model, and combined crossover and mutation strategy with fitness of population model in order to improve rate of convergence and avoid the appearance of local extremum. In AC-GADA, the pheromone was desicided not only by the number of ants which have chosen the path but also the pheromone code of each ant. The comparison with ACDA(Ant Colony Data Association) and JPAD(Joint Pobabilistic Data Association) proved its efficiency and superiority.
For large scale integrated circuit fault diagnosis problem, this paper presents AC-GATSG algorithm. The algorithm converts scale integrated circuit into a graph, and conducts the study of automatic generation by using the advantage AC algorithm. In AC-GATSG, the individual stained will be reconstructed when optimal solution occurs in each generation, and the pheromone will be updated at the same time. Mutation probability of individual mutations and threshold which limits generated during crossover are useful elements in AC-GATSG which reduce the execution of generating local minimum one by one. We select combinational circuits and typical flight simulator circuit units as experiment objects, and the results show that AC-GATSG algorithm can satisfy the requirement of practical production, and has a widely application prospect.
The test set of large scale intefrated circuit, which is generated automatically, has a large number of redundant vectors that cause high waste of resources and low efficiency of production and development in standardized tests. This paper presents AC-SATSO to optimize the sets of the circuit tests. AC-SATSO builds the model of test vector nodes and fault nodes hierarchically, and identify these nodes with in-out degree. AC-SATSO algorithm combines AC with simulated annealing algorithm to accelerate the convergence rate and determine whether to accept the new population with Boltzmann mechanism. Under this optimization strategy, AC-SATSO can make a great convergence rate although lacking of pheromone at the beginning, and avoid the appearance of local minimum in subsequent stage. Besides, this paper puts forward the concept of optimum complete test set which complete the test with the lowest consumption. The result of experiment demonstrated that AC-SATSO method of solution quality and stability of the algorithm are better than literature. In recent years, researches on ant colony algorithm, as well as its applications, have been paid great attentions by many researchers globally. Based on ant colony algorithm, genetic algorithm and simulated annealing algorithm, this paper focuses on the research of flight simulator, and proposes several optimization methods. There are both theoretical and practical significance of this research on the improvement of ant colony algorithm.
蚁群算法源于人们对生物界蚁群觅食行为的观察和模拟,相较于传统的优化方法,蚁群算法在解决各类优化问题时具有很强的适应性和鲁棒性,适用于各类组合优化问题。蚁群算法在使用过程中参数设置简单,易于实现,因此在众多科学研究以及生产制造领域得到了广泛的应用,本文以蚁群算法为基础同时结合遗传算法和模拟退火算法两种仿生算法,对飞行模拟器平台中的多目标数据关联问题,数字电路测试集生成以及测试矢量集合优化问题进行了一定程度的研究,并且通过相关实验将本文算法与现有的一些算法进行了对比,实验结果表明,本文提出的几种算法能有效的提高蚁群算法的收敛速度,克服局部极值的出现,算法在迭代次数和执行效率上都体现出了很大的优势,本文的具体研究内容如下:
1.以多传感器多目标数据关联问题为研究对象,提出了一种基于蚁群-遗传算法的AC-GADA算法,该算法结合了遗传算法的染色体交叉变异思想,为蚁群中的个体赋予了单独的染色体,同时对蚁群算法的信息素模型进行了改进,在每一代种群寻优之后,利用交叉变异机制对蚁群进行重新构造,同时结合了控制种群适应度的方法,进一步做到了个体差异化,有效地避免了局部极值的出现,在AC-GADA算法中,信息素强度不仅与途经该路径的蚂蚁数目相关,同时还受蚂蚁自身的信息素编码影响,因此AC-GADA算法具有更强的随机搜索能力,通过大量的实验表明AC-GADA算法在收敛速度、关联的准确率和成功率上都有明显的优势。
2.针对大规模集成电路的故障诊断问题,提出了AC-GATSG算法,该算法首先将大规模集成电路的关联矩阵转化为图结构,从而利用蚁群算法解决图着色问题上的优势,对电路测试集的自动生成问题进行探索实现,在处理图节点着色问题时同样引入了交叉变异机制,在每代的遍历过程出现最优解时对蚁群个体进行染色重构,同时更新信息素,并将阈值引入信息素扩散模型中,利用阈值限定了交叉变异过程中产生突变个体的几率,降低了算法执行过程中由一个局部极值陷入另一个局部极值的可能。在对比实验中选取了标准组合电路以及典型的飞行模拟器电路单元为对象,通过与一些经典算法以及以仿生学算法为基础的文献算法对比,验证了AC-GATSG算法的有效性,AC-GATSG算法能够满足模拟器生产实践中对基础电路单元的测试集生成需求,并且具有广阔的应用前景。
3.自动生成的大规模集成电路测试集往往存在冗余问题,在进行标准化测试时,这些冗余矢量会造成大量的资源浪费,影响生产和研发的效率,针对上述问题本文提出了AC-SATSO算法对电路测试集进行优化,AC-SATSO算法首先对故障节点与测试矢量节点进行分层建模,并通过故障入度和测试矢量出度对双层节点进行标识,AC-SATSO算法利用模拟退火算法的扰动机制加速种群的收敛速度,并通过Boltzmann机制判断是否接纳新的种群,这种寻优策略在算法执行初期可以避免由于缺乏信息素而导致的寻优速度慢的问题,后期则有效的限制了局部极值的出现并且同时保留了优良的数据集,随着迭代次数的增加,模拟退火算法保留优秀结果的概率也大大增加,同时也加速了算法的收敛。并且针对生产实践中的测试环节本文提出了最优完备测试集的概念,即所需系统开销最少的完全测试集,通过不同规模的测试集优化对比试验证明,AC-SATSO算法在求解质量和稳定性上都优于文献算法。
近年来,蚁群算法等仿生算法的研究受到了国内外学者的关注,并且涌现出了大量的改进算法和新的领域应用,本文以蚁群算法为基础,结合遗传算法、模拟退火算法在飞行模拟器平台中展开了一些研究,提出了几种相互结合的优化算法,在蚁群算法的改进以及更丰富领域的应用上,其研究具有一定的实际意义。
In twenty-first century, the development of national Air Force represent the level of modernization of the military construction, and the flight simulator provides the complete technical support for Air Force training system, there are a lot of advantages of flight simulator such as saving training fund, protecting pilots’lives and performing special and complex training, and we have carried out a large number of research projects of flight simulator. The flight simulator is composed by platform system, computer system, visualization system, electronic controling system and console system. With the years of experiences, the platform system, computer system and visualization system have become increasingly stable. The group operation in complex situation is a very urgent problem that needs to be solved due to the requirement of higher combat abilities of flight simulator. A increasing number of integrated circuits also increase the difficulty of maintain flight simulator, and fault diagnosis of large-scale integrated circuits has became an international issue.
Ant colony algorithm originated from the observation and simulation of the ants’behaviors. Compared with the traditional optimization methods, AC is widely applied in scientific research and industrial production in virtue of their higher adaptability, robustness and parallel processing capability. This paper ccommence the study in multi-target data association, digital circuit test set generation and test set optimization with AC combined with genetic algorithm and simulated annealing algorithm. The result of experiments has shown that AC-GADA, AC-GATSG and AC-SATSO can effectively improve the convergence speed of the ant colony algorithm, and overcome the emergence of local extremum. The main contents of this thesis are as follows:
For multi-sensor multi-target data association problem, this paper presents an AC-GADA algorithm which combines ant colony algorithm with genetic algorithm. This algorithm designed difference pheromone for each ant and improves global pheromone increment model, and combined crossover and mutation strategy with fitness of population model in order to improve rate of convergence and avoid the appearance of local extremum. In AC-GADA, the pheromone was desicided not only by the number of ants which have chosen the path but also the pheromone code of each ant. The comparison with ACDA(Ant Colony Data Association) and JPAD(Joint Pobabilistic Data Association) proved its efficiency and superiority.
For large scale integrated circuit fault diagnosis problem, this paper presents AC-GATSG algorithm. The algorithm converts scale integrated circuit into a graph, and conducts the study of automatic generation by using the advantage AC algorithm. In AC-GATSG, the individual stained will be reconstructed when optimal solution occurs in each generation, and the pheromone will be updated at the same time. Mutation probability of individual mutations and threshold which limits generated during crossover are useful elements in AC-GATSG which reduce the execution of generating local minimum one by one. We select combinational circuits and typical flight simulator circuit units as experiment objects, and the results show that AC-GATSG algorithm can satisfy the requirement of practical production, and has a widely application prospect.
The test set of large scale intefrated circuit, which is generated automatically, has a large number of redundant vectors that cause high waste of resources and low efficiency of production and development in standardized tests. This paper presents AC-SATSO to optimize the sets of the circuit tests. AC-SATSO builds the model of test vector nodes and fault nodes hierarchically, and identify these nodes with in-out degree. AC-SATSO algorithm combines AC with simulated annealing algorithm to accelerate the convergence rate and determine whether to accept the new population with Boltzmann mechanism. Under this optimization strategy, AC-SATSO can make a great convergence rate although lacking of pheromone at the beginning, and avoid the appearance of local minimum in subsequent stage. Besides, this paper puts forward the concept of optimum complete test set which complete the test with the lowest consumption. The result of experiment demonstrated that AC-SATSO method of solution quality and stability of the algorithm are better than literature. In recent years, researches on ant colony algorithm, as well as its applications, have been paid great attentions by many researchers globally. Based on ant colony algorithm, genetic algorithm and simulated annealing algorithm, this paper focuses on the research of flight simulator, and proposes several optimization methods. There are both theoretical and practical significance of this research on the improvement of ant colony algorithm.
引文
[1]王行仁.飞行实时仿真系统及技术[M].北京:北京航空航天大学出版社,1998.
[2]陈绍祖.林克飞行模拟器[J].模拟技术,1989(4):24.
[3]李学国.飞行训练模拟器的发展[J].国际航空,1989(4):45-47.
[4] Allen L D. Evolution of Flight Simulator[M]. AIAA-93-354-CP,1993.
[5]梁恺.航空仿真技术[J].测控技术,1998(3):8-10.
[6]章晨. 90年代飞行模拟器的发展动向[J].国际航空. 1992,(7):10-11.
[7] Daniel Lazard, Jean-Pierre Merlet. The (true) Stewart Plarform has 12 configurations[C]. IEEE Conf. on Robotic and Automation. 1994,3:2160-2165.
[8] Carlo Innocenti, Vincenzo Parenti-Castelli, Direct Position Analysis of the Stewart Platform[J]. Mech. Mach. Theory. 1990, 25(6):611-621.
[9] Chang-de Zhang, Shi-Min Song. Forward Kinematics of a Class of Parallel (Stewart) Platform with Closed-Form Solutions[C]. Proceedings of the 1991 IEEE, International Conference on Robotics and Automation, Sacrameto, California. April 1991:2676-2681.
[10] S.V. Screenivasan, K.J. Waldron, P. Nanua. Colsed Form Direct Displacement Analysis of a 6-6 Stewart Platform. Mech[J]. Mach. Theory 1994, 29(6):855-864.
[11] J.-P. Merlet. Direct Kinematics and Assembly Modes of Parallel manipulators[J]. The International Jouranl of Robotics Research. 1992,8(5):150-162.
[12] Charles W. Wampler. Forward Displacement Analysis of General Six-In-Parallel SPS(stewart) Platform manipulators Using Soma Coordinates[J]. Mech. Mach. Theory 1996, 33(3):331-337.
[13] M. Raghavan. The Stewart platform of General Geometry Has 40 Configurations[J]. Transction of the ASME Journal of Mechanical Design. JUNE 1993, 115:277-282.
[14]姜虹,贾嵘,董宏智.六自由度并联机器人位置正解的数值解法[J].上海交通大学学报,2000,34(3):351-353.
[15] K.H. Hunt, E.F.J. Primrose. Assembly configurations of Some In-Parallel-Actuated Manipulators[J]. Mech.Mach.Theory 1993,28(1)31-42P.
[16]黄真,孔宪文.6-SPS并联机器人机构运动分析[J].东北重型机械学院学报,1997,16(2):189-194页.
[17]李鹭扬,吴洪涛.并联机构正运动学一维搜索法[J],扬州大学学报(自然科学版),2000,3(1):66-70.
[18]吴军,李铁民,关立文.飞行模拟器运动平台的计算力矩控制[J],清华大学学报(自然科学版,2006,46(8)1405-1408.
[19]吴东苏,顾宏斌.飞行模拟器运动平台关节空间自适应模糊控制[J],南京航空航天大学学报,2008,40(5)670-676.
[20]吴东苏,顾宏斌.轻型飞行模拟器运动平台自适应模糊反步控制[J],仪器仪表学报,2008,29(7),1464-1469.
[21]丁东风.飞行模拟器仪表的计算机图形实现[J],计算机应用,1993,6,41-43.
[1]王行仁.飞行实时仿真系统及技术[M].北京:北京航空航天大学出版社,1998.
[2]陈绍祖.林克飞行模拟器[J].模拟技术,1989(4):24.
[3]李学国.飞行训练模拟器的发展[J].国际航空,1989(4):45-47.
[4] Allen L D. Evolution of Flight Simulator[M]. AIAA-93-354-CP,1993.
[5]梁恺.航空仿真技术[J].测控技术,1998(3):8-10.
[6]章晨. 90年代飞行模拟器的发展动向[J].国际航空. 1992,(7):10-11.
[7] Daniel Lazard, Jean-Pierre Merlet. The (true) Stewart Plarform has 12 configurations[C]. IEEE Conf. on Robotic and Automation. 1994,3:2160-2165.
[8] Carlo Innocenti, Vincenzo Parenti-Castelli, Direct Position Analysis of the Stewart Platform[J]. Mech. Mach. Theory. 1990, 25(6):611-621.
[9] Chang-de Zhang, Shi-Min Song. Forward Kinematics of a Class of Parallel (Stewart) Platform with Closed-Form Solutions[C]. Proceedings of the 1991 IEEE, International Conference on Robotics and Automation, Sacrameto, California. April 1991:2676-2681.
[10] S.V. Screenivasan, K.J. Waldron, P. Nanua. Colsed Form Direct Displacement Analysis of a 6-6 Stewart Platform. Mech[J]. Mach. Theory 1994, 29(6):855-864.
[11] J.-P. Merlet. Direct Kinematics and Assembly Modes of Parallel manipulators[J]. The International Jouranl of Robotics Research. 1992,8(5):150-162.
[12] Charles W. Wampler. Forward Displacement Analysis of General Six-In-Parallel SPS(stewart) Platform manipulators Using Soma Coordinates[J]. Mech. Mach. Theory 1996, 33(3):331-337.
[13] M. Raghavan. The Stewart platform of General Geometry Has 40 Configurations[J]. Transction of the ASME Journal of Mechanical Design. JUNE 1993, 115:277-282.
[14]姜虹,贾嵘,董宏智.六自由度并联机器人位置正解的数值解法[J].上海交通大学学报,2000,34(3):351-353.
[15] K.H. Hunt, E.F.J. Primrose. Assembly configurations of Some In-Parallel-Actuated Manipulators[J]. Mech.Mach.Theory 1993,28(1)31-42P.
[16]黄真,孔宪文.6-SPS并联机器人机构运动分析[J].东北重型机械学院学报,1997,16(2):189-194页.
[17]李鹭扬,吴洪涛.并联机构正运动学一维搜索法[J],扬州大学学报(自然科学版),2000,3(1):66-70.
[18]吴军,李铁民,关立文.飞行模拟器运动平台的计算力矩控制[J],清华大学学报(自然科学版,2006,46(8)1405-1408.
[19]吴东苏,顾宏斌.飞行模拟器运动平台关节空间自适应模糊控制[J],南京航空航天大学学报,2008,40(5)670-676.
[20]吴东苏,顾宏斌.轻型飞行模拟器运动平台自适应模糊反步控制[J],仪器仪表学报,2008,29(7),1464-1469.
[21]丁东风.飞行模拟器仪表的计算机图形实现[J],计算机应用,1993,6,41-43.
[39] Maniezzo V, Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem[D]. Techniacl Report CSR 98-1, Scienze dell Informazione, Universita di Bologna, Sede di Cesena, Italy, 1998.
[40] Maniezzo V, Colorni A. The ant system applied to the quadratic assignment problem[J]. IEEE Transactions on Knowledge and Data Engineering, 1999, 11(5):769-778.
[41] Colorni A, Dorigo M, Maniezzo, et al, Ant system for job-shop scheduling[J]. Belgian J. Oper. Res. Statist. Comput. Sci. 1994, 34:39-53.
[42] Stutzle T. An ant approach to the flow shop problem[D]. Technical Teport AIDA-97-07, FG Intellektik, FB Informatik, TH Darmstadt, September 1997.
[43] Besten M den, Stutzle T, Dorigo M. Scheduling Single machines by ants[D]. Technical Report IRIDIA/9916, IRIDIA, Universite Libre de Bruxelles, Belgium,1999.
[44] Bullnheimer B, Hartl R G, Strauss C. An improved ant system algorithm for the vehicle routing problem[D]. Technical Report POM-10/97, Institute of Management Science, University of Vienna, Austria, 1997, Ann. Oper.Res. 89,1999.
[45] Bullnheimer B, Hartl R F, Strauss C. Applying the ant system to the vehicle routing problem[C]. Metaheuristics:Advances and Trends in Local Search Paradingms for Optimization, Kluwer Academic Publishers, Boston, MA, 1999, 285-296.
[46] Gambardella L M, Taillard E, Agazzi G. MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows[J]. New Ideas in Optimization, McGraw-Hill, London, UK, 1999, 63-76.
[47] Gambardella L M, Dorigo M, HAS-SOP: a hybrid ant system for the sequential ordering problem[D]. Technical Report IDSIA-11-97, IDSIA, Lugano, Switzerland.1997.
[48] Costa D, Hertz A. Ants can colour graphs[J]. Journal of the Operational Research Society, 1997, 48(3):295-305.
[49] Ahn S H, Lee S G, Chung T C, Modified ant colony system for coloring graphs[C]. Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and the 4th Pacific Rim Conference on Mutimedia, 2003,3,1849-1853.
[50] Ramalhinho Lourenco H, Serra D. Adaptive approach heuristics for the generalized assignment problem[D]. Technical Report EWP Series No.304, Department of Economics and Management, Universitat Pompeu Fabra, Barcelona,1998.
[51] Leguizamon G, Michalewicz Z. A new version of ant system for subset problems[C]. Proceedings of the 1999 Congress on Evolutionary Computation, 1999, 1459-1464.
[52] Parra-Hernandez R, Dimopoulos N. On the performance of the ant colony system for solving the multidimensional knapsack problem[C]. Proceedings of the 2003 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2003,1:338-341.
[53] Liang Y C, Smith A E. An ant system approach to redundancy allocation[C]. Proceedings of the 1999 Congress on Evolutionary Computation, 1999, 2:1478-1484.
[54] Liang Y C, Smith A E. An ant colony optimization algorithm for the redundancy allocation problem(RAP)[J]. IEEE Transactions on Reliability, 2004, 53(3):417-423.
[55] Bauer A, Bullnheimer B, Hartle R F, et al. An ant colony optimization approach for single machine total tardiness problem[C]. Proceedings of the 1999 Congress on Evolutionary Computation, 1999, 1445-1450.
[56] Chang C S, Tian L, Wen F S, New approach to fault section estimation in power systems using ant system[J]. Electric Power Systems Research, 1999,49(1):63-70.
[57]孙京诰,李秋艳,杨欣斌等.基于蚁群算法的故障识别[J].华东理工大学学报,2004,30(2):194-198.
[58] Schoofs L, Naudts B. Ant coonies are good at solving constraint satisfaction problems[C]. Proceedings of the 2000 Congress on Evolutionary Computation,2000,2:1190-1195.
[59] Solnon C. Ant can solve constraint satisfaction problems[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(4):347-357.
[60] Fu S G, Dozier G. Solving distributed constraint satisfaction problems with an ant-like society of hillclimbers[C]. Proceedings of the International Conference on Artificial Intelligence, 2003,1:263-369.
[61] Mertens K, Holvoer T,CSAA: a distributed ant algorithm framework for constraint satisfaction[C]. Proceedings of the 17th International Florida Artificial Intelligence Research Society Conference, 2004, 2:764-769.
[62] Mathur M,Karale S B, Priye S,et al. Ant colony approach to continuous function optimization[J]. Industrial and Engineering Chemistry Research, 2000,39(10):3814-3822.
[63]魏平,熊伟清.用于一般函数优化的蚁群算法[J].宁波大学学报,2001,14(4):52-55.
[64] Abbaspour K C, Schulin R, Van Genuchten M Th. Estimating unsaturated soil hydraulic parameters using ant colony optimization[J]. Advances in Water Resources, 2001,24(8) :827-841.
[65]汪镭,吴启迪.蚁群算法在系统识别中的应用[J].自动化学报,2003,29(1):102-109.
[66] Duan H B, Wang D B, Zhu J Q, et al. Parameter identification of LuGre friction model for flight simulation servo system based on ant colony algorithm[J]. Transactions of Nanjing University of Aeronautics&Astronautics,2004,21(3):179-183.
[67] Parpinelli R S, Lopes H S, Freitas A A. Data mining with an ant colony optimization algorithm[J]. IEEE Transactions on Evolutionary Computation, 2002,6(4):321-332.
[68] Meshoul S, Batouche M. Ant colony system with extremal dynamics for point matching and pose estimation[C]. Proceeding of the 16th International Conference on Pattern Recogintion, 2002, 3:823-826.
[69] Zheng G, Wong A, Nahavandi S. Hybrid ant colony algorithm for texture classification[C]. Proceeding of the 2003 Congress on Evolutionary Computation, 2003, 4:2648-2652.
[70] Kumar V, Sahin F. Foraging in ant colonies applied to the mine detection problem[C]. Proceedings of the IEEE International Workshop on Soft Computing in Industral Applications, 2003, 6.
[71] Kumar V, Sahin F. Foraging in ant colonies applied to the mine detection problem[C]. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, 6:5429-5434.
[72] Chapman E, Sahin F. Application of swarm intelligence to the mine detection problem[C]. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, 6:5429-5434.
[73] Shmygelska A, Aguine-Hemundez R, Hoos H H. An ant colony algorithm for the 2D HP protein folding problem[C]. Proceedings of the 3rd International Workshop on Ant Algotithms/ANTS2002, Lecture Notes in Computer Science, 2002, 2463:40-52.
[74] Li Y M, Xu Z B, An ant colony optimization heuristic for solving maximum independent set problems[C]. Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications, 2003, 206-211.
[75] Korosec P, Silc J, Robic B. Solving the mesh-partitioning problem with an ant-colony algorithm[J]. Parallel Computing, 2004, 30(5-6):785-801.
[76]曹春红,卢奕南,李文辉.改进的蚂蚁算法在几何约束求解中的应用[J].工程图学学报,2003,25(4):46-50
[77]曹春红,李文辉,张永坚.遗传蚂蚁算法在几何约束求解中的应用[J].仪器仪表学报,2003,25(4):393-396
[78]高尚,杨静宇,吴小俊.圆排列问题的蚁群模拟退火算法[J].系统工程理论与实践,2004,(8):102-106
[79] Jensen R, Shen Q, Fuzzy-rough data reduction with ant colony optimization[J]. Fuzzy Sets and Systems, 2005, 149(1):5-20.
[80]段海滨,蚁群算法及其在高性能电动仿真转台参数优化中的应用研究[D],南京:南京航空航天大学博士学位论文,2005.
[81]何靖华.面向仿生制造的蚂蚁算法分析研究及应用[D],武汉:华中科技大学硕士学位论文,2002.
[82] Holland JH. Adaptation in natural and artificial systems [D]. Ann Arbor: University of Michigan Press; 1975.
[83] Konak A., D.W. Coit, and A.E. Smith. Multi-objective optimization using genetic algorithms: A tutorial [J]. Reliability Engineering and System Safety, 2006, 91(9): 992-1007.
[84] S.Kirkpatrick, C.D.Gelatt, and M.P.Vecchi, Optimization by Simulated Annealing[J], Science 220,1983:637-680.
[85] Garey.M.R. nd D.S.Johnson, Computers and Intractability: a guide to the theory of NP-completeness[J]. Freeman, San Francisco, 1979.
[86] M.Lundy and A.Mess, Convergence of an Annealing Algorithm[J], Mathematical programming, Vol 34,1986:111-124.
[87] A.Casotto, F.Romeo, and A.Sangiov anni-Vincetelli, A parallel Simulated Anneling Algorithm for Placement of Macro-Cells[C], IEEE Trans, Computer-Aided Design,Vol.6,No.5,1987:837-847.
[88] Hao Chen,Nicholas S.Flann and Daniel W.Watson, Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Algorithm, Parallel and Distributed System, Vol.9 No.2,Feb.1998:126-136.
[89]何友,王国宏,陆大金.多传感器信息融合及应用[M],电子工业出版社2000.
[90]孙全.多传感器信息融合与多目标跟踪系统的研究[D],浙江大学博士学位论文,2000.
[91] Moose R L. An adaptive state estimation solution to the maneuvering target problem[C]. IEEE Trans. On Automatic Control, 1975, 20(6):359-362.
[92] Kifubarajan T, Bar-Shalom Y, Pattipai K R, Kadar I. Ground target tracking with variable structure IMM estimator[C]. IEEE Trans. On Aerospace and Electronic Systems. 2000, 36(1):26-46.
[93]周宏仁,敬忠良,王陪德.机动目标跟踪[M],北京:国防工业出版社,1991:10-21,219-233
[94] Sittler R W. An optimal data association problem in surveillance theory[C]. IEEE Military Electronics, 1964, 8(2):125-139.
[95] Kifubarajan T, Bar-Shalom Y, Pattipai K R, Kadar I. Ground target tracking with variable structure IMM estimator[C]. IEEE Trans. On Aerospace and Electronic Systems. 2000, 36(1):26-46.
[96] Jikkov V P, Angelova D S, Sernerdije T A. Design and comparidon of mode-set adaptive IMM algorithms for maneuvering target tracking[C]. IEEE Trans. On Aerospace and Electronic Systems. 1999, 35(1):343-350.
[97] Balckman S S. Multiple Hypothesis tracking for multiple target tracking[J]. Aerospace and Electronic Systems.2004, 19(1):5-18.
[98] Zhang X, Willett P, Bar-Shalom Y. Uniform versus nonuniform sampling when tracking in clutter[J]. Aerospace and Electronic Systems. 2006, 42(2):388-400.
[99] Stubberud S C, Kramer K A. Data association for multiple sensor types using fuzzy logic[C]. IEEE Trans. On Instrumentation and Measurement. 2006, 55(6):2292-2303.
[100] Long-Huai Wang, Hsueh-Jyh Li. Using range profiles for data association in multiple-target tracking[C]. IEEE Trans. On Aerospace and Electronic Systems. 1996,32(1) :444-450.
[101] Challa S, Ba-Ngu Vo, Xuezhi Wang. Bayesian Approaches to track existence to track existence-IPDA and random sets[C]. 5th International Conference on Information fusion, 2002, 2:1228-1235.
[102] Ba-Ngu Vo, Singh S. On the Bayes filtering equations of finite set statistics[C]. Proc. 5th Asian Control Conference, 2004, 2:1264-1269.
[103] Vermaak J, Godsill S J, Perez P. Monte Carlo filtering for multi-target tracking and data association[C]. IEEE Trans on Aerospace and Electronic Systems. 2005, 41(1):309-332.
[104] Miguez J, Artes-Rodriguez A. A Monte Carlo method for joint node location and maneuvering target tracking in a sensor netwoerk.[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, 2006, 4:989-922.
[105] Soohl Jeong, Tugnait J K. Multisensor tracking of a maneuvering target in clutter using IMMPDA filter with simultaneous measurement update[C]. IEEE Trans. On Aerospace and Electronic. 2005, 41(3):1122-1131.
[106] Megherbi N, Ambellouis S, Colot O, Cabestaing F. Data association in multi-target tracking using belief theory: handling target emergence and disappearance issue[C]. IEEE Conference on Advanced Video and Signal Based Surveillance, Como, Italy, 2005:517-521.
[107] Taek Lyul Song, Dong Gwan Lee. A probabilistic nearest neighbor filter algorithm for validated measurements[C]. IEEE Trans. On Signal Processing, 2006, 54(7):2797-2802.
[108] Michael Kalandros, Lucy Y Pao. Multisensor covariance control strategies for reducing bias effects in interacting target scenarios[C]. IEEE Trans. On Aerospace and Electronic Systems. 2005, 41(1):153-173.
[109] Huimin Chen, X Rong Li, Yaakov Bar-Shalom. On joint track initiation and parameter estimation under measurement origin uncertainty[C]. IEEE Trans. On Aerospace and Electronic Systems, 2004, 40(2):675-694.
[110] Matt L Milier, Harold S Stone, Ingemer J Cox. Optimizing Murty’s ranked assignment method[C]. IEEE Trans. On Aerospace and Electronic Systems.1997, 33(3):851-862.
[111]汪圣利.跟踪密集多回波环境下多机动目标的IMM-PC算法[J],自动化技术与应用,2004,23(7):1-3.
[112] REID D. An algorithm for tracking multiple target [J]. IEEE Trans on Automat and Contr , 1979 ,24 (6) :84 - 90.
[113] SAMUEL BLACKMAN, ROBER POPOLI, Design and Analysis of Modern Tracking Systems [M]. Boston :Artech House ,1999.
[114] DEB S, YEDDANAPUDI M, PATTIPATI K R. A generalized S-D assignment algorithm for multisensor-multitarget state estimation [J]. IEEE Trans on AES, 1997,33 (2) :523 - 538.
[115] DEB S, PATTIPATI K R, BAR-SHALOM Y, A multisensor-multitarget dataassociation algorithm for heterogeneous sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(2):560-568.
[116] BLAIR W R, WASTON G A, KIRUBARAJAN T. Benchmark for radar allocation and tracking in ECM [J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4):1097-1114.
[117] KIRUBARAJAN T, BAR-SHALOM Y, BLAIR W R. IMMPSAF for radar management and tracking benchmark with ECM [J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4):1115-1133.
[118] DAEIPOUR E, BAR-SHALOM Y, IMM tracking of maneuvering targets in the presence of glint [J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(3):996-1003.
[119]康莉,谢维信,黄敬雄.一种基于蚁群算法的多目标跟踪数据关联方法[J] .电子学报,2008,36 (3) : 586-589.
[120]朱力立,张焕春,经亚枝.一种基于模糊自适应GA的广义S维分配算法[J].模式识别与人工智能,2004 ,17( 1):82-86.
[121]胡炜薇,赵坤,杨莘元.基于粒子群算法的广义S维分配算法[J].导弹与制导学报,2005, 25(4):791-795.
[122]肖宏峰,谭冠政.基于遗传算法的混合蚁群算法[J].计算机工程与应用, 2008, 44(16), 42-45.
[123]刘波,杨路明,雷刚越.融合粒子群与蚁群算法优化XML群体智能搜索[J],计算机研究与发展, 2008, 45(8), 1371-1378.
[124] Konak A.,D.W. Coit and A.E. Smith. Multi-objective optimization using genetic algorithms: A tutorial [J]. Reliability Engineering and System Safety, 2006, 91(9): 992-1007.
[125] Roth J P, Bouricius W G, Schneider P R. Programmed algorithms to computer tests to detect and distinguish between failures in logic circuits[J]. IEEE Transactions on Electronic Computers, 1967, EC-16:567-580
[126]杨士元.数字系统的故障诊断与可靠性设计[M].北京:清华大学出版社,2000.39-49.
[127] Goel P. An implicit enumeration algorithm to generation test for combinational circuits[J]. IEEE Transactions on Computers. 1981, C-30(3):215-222.
[128] Fujiwara H. FAN: a fanout-oriented test pattern generation algorithm[C]. 1985 international Symposium on Circuits and Systems, Kyoto, Japan, 1985, 2:671-674.
[129] Schulz M H, Trischler E, Sarfert T M, SOCRATES: A highly efficient automatic test pattern generation system[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 1988,7(1):126-136.
[130] Gizdarski E, Fujiwara H, SPIRIT: A highly robust combinational test generation algorithm[J]. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems. 2002,21(12):1446-1458.
[131] Tafertshofer P, Ganz A, Henftling M. A sat-based implication enging for efficient ATPG, equivalence checking, and optimization of netlists[C]. Proceedings of the International Conference on Computer-Aided Design, 1997:648-655.
[132] Hayashi T, Kita H, Hatayama K. A genetic approach to test generation for logic circuits[C]. Proceedings of the Third Asian Test Symposium, Nara, Japan, 1994:101-106.
[133] Long Wangning, Yang Shiyuan, Min Yinghua, et al. Level-oriented GA-based test generation of logic circuits[C]. Proceedings of the IEEE International Conference on Intelligent Processing Systems, Beijing, China, 1998, 1:563-567.
[134]潘中良,张光昭.数字电路多故障测试生成的神经网络方法研究[J].仪器仪表学报,1999,20(3):232-234.
[135]刘小东,孙圣和.基于神经网络的组合电路测试生成算法[J].哈尔滨工业大学学报, 2002, 4:255-257.
[136]徐建斌,李智.神经网络在组合电路故障模拟测试生成算法中的应用[J].电路与系统学报,2001,6(4):109-110.
[137]候艳丽.数字集成电路测试生成算法研究[D],哈尔滨工业大学,博士学位论文,2008.
[138] W.B. Jone and C. A. Papachristou, Acoordinate Circuit Partitioning and Test Generation Method for Pseudo-Exhaustive Testing of VLSI Circuits[J], IEEE Trans. On CAD, vol.14, no.3, 374-384P, Mar, 1995.
[139] K. T. Cheng, C. J. Lin, Timing-Driven Test Point Insertion for Full-Scan and Partial-Scan BIST[J], Proc. Of International Test Conf., 506-514P, 1995,10.
[140] E. J. McCluskey, Verification Testing-A Pseudoexhaustive Test Technique[J], IEEE Trans. On Computers, vol.C-33, no.6, 541-546P,1984,06.
[141] E. J. McCluskey, S. Bozorgui-Nesbat, Design for Autonomous Test[J], IEEE Trans. On Computers vol.C-30, no.11, 866-875P,1981,11.
[142] M.W. Roberts and P.K. Lala, An Algorithm for the Patritioning of Logic Circuits[J], IEEE Proc., vol.131,pt.E,no.4,1984,07.
[143] R.Srinivasan, S.K. Gupta and M.A. Breuer, An Efficient Partitioning Strategy for Pseudo-Exhaustive Testing[J], Proc. Of Design Automation Conf., 242-248, 1993,06.
[144] S.C. Chang and J.C. Rau, An Timing Driven Pseudo Exhaustive Testing for VLSI Circuits[C]. Departmenti of Computer Scient and Information Engineering National Chung-Cheng University Chiayi, Taiwan, R.O.C.
[145] Gizdarski E, Fujiwara H. SPIRIT: a highly robust combinational test generation algorithm[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2002, 21(12):1446-1459.
[146] Hamzaoglu I, Patel J H. New techniques for deterministic test pattern generation[J]. Jouranl of Electronic Testing: Theory and Applications. 1999,15:63-73.
[147] Saab D G, Saab Y G, Abraham J A. CRIS: A test cultivation program for sequential VLSI circuits[J]. 1992 IEEE/ACM International Conference on Computer-Aided Design, Santa Clara, AC, USA, 1992:216-219.
[148]朱虎,宋恩民,路志宏.求解图着色问题的最大最小蚁群搜索算法[J].计算机仿真,2010,27(3):190-192.
[149]廖飞雄,马良.图着色问题的启发式搜索蚂蚁算法[J].计算机工程,2007,33(16):191-195.
[150] Jinno C, Inoue M, Fujiwara H. Internally balanced structrue with hold and switching functions[J]. Transactions of the Institute of Electronics, Information and Communication Engineers D-I. 2003, J86D-I(9):682-690.
[151] Fujiwara H. The complexity of fault detection problems for combinational logic circuits[J]. Transactions of the Information Processing Society of Japan. 1986, 27(8):768-773.
[152] Hamzaoglu I, Patel J H. Test set compaction algorithms for combinational circuits[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2000, 19(8):957-963.
[153] Lu Changhua, Zhang Qibo, Jiang Weiwei. A global optimization algorithm for the minimum test set of circuits[C]. IEEE International Conference on Robotics, Intelligent Systems and Signal Proceeding, Changsha, Hunan, China, 2003, 2:1203-1207.
[154] Sanchez E, Reorda M S, Squillero G. Efficient techniques for automatic verification-oriented test set optimization[J]. International Journal of Parallel Programming. 2006, 34(1):93-109.
[155] Qiao Jia-Qing, Fu Ping, Yin Hong-Tao. Test set optimization based on genetic reordering[J]. Tieh Tzu Hsueh Pao. 2007,35(12):2335-2338.
[156] Santoso Y, Merten M, Rudnick E M, et al. FreezeFrame: Compact test generation using a frozen clock strategy[J]. Design, Automation and Test in Europe Conference and Exhibition, Munich, Germany, 1999:747-752.
[157] Dimopoulos M, Linardis P. Efficient static compaction of test sequences sets through the application of set covering techniques[J]. Proceedings of Design, Automation and Test in Europe Conference and Exhibition, Paris, France, 2004:194-199.
[158] Islam S Z, Ali M A M. Test pattern optimization using proper seed selection in mixed-mode technique[C]. Proceedings of Third IEEE International Workshop on Electronic Design, Test and Application, Kuala Lumpur, Malaysia, 2006:105-109.
[159] Fukunaga M, Kajihara S, Xiaoqing Wen, et al. A dynamic test compaction procedure for high-qualit path delay testing[C]. Proceedings of Asia and south Pacific Design Automation Conference, Yokohama, Japan, 2006:348-353.
[160] Hsiao M S, Rudnick E M, Patel J H. Fast static compaction algorithm for sequential circuit test vectors[J]. IEEE Transactions on Computers. 1999, 48(3):188-195.
[161]杨士元.数字系统的故障诊断与可靠性设计[M].北京:清华大学出版社,2000.94– 102.
[162] Pomeranz I, Reddy S M. On the compaction of test sets produced by genetic optimization[J]. Proceedings of Sixth Asian Text Symposum, Akita, Japan, 1997:4-9.
[163] Bossen D C,HongSJ.eause-effect analysis for multiple fault detection in combinational networks[J]. IEEE trans.OnComPuter.11.1971.
[164] Agrawa1P,Agrawal V D.Probabilistic analysis of random test generation method for irredundant combinational logic network[J]. IEEETrans.ComPut.1972, C-21.
[165] W.B.Jone and C.A.PaPaehristou.A Coordinate Cireuit Partitioning and Test Generation Method for Pseudo-Exhaustive Testing of VLSI Circuits[J]. IEEE Trans.on CAD.vol.14,no.3,374-384, Mar.1995.
[166]俞龙江,彭喜源,彭宇.基于蚁群算法的测试集优化[J].电子学报,2003,31(8) :1178 - 1181.
[167]王小港.遗传算法在VLSI设计自动化中的应用研究[D] . 2001.
[168]乔家庆,付平,尹洪涛.基于遗传排序的测试集优化[J]电子学报, 2007, 35 (12) : 2335 - 2338.
[2]陈绍祖.林克飞行模拟器[J].模拟技术,1989(4):24.
[3]李学国.飞行训练模拟器的发展[J].国际航空,1989(4):45-47.
[4] Allen L D. Evolution of Flight Simulator[M]. AIAA-93-354-CP,1993.
[5]梁恺.航空仿真技术[J].测控技术,1998(3):8-10.
[6]章晨. 90年代飞行模拟器的发展动向[J].国际航空. 1992,(7):10-11.
[7] Daniel Lazard, Jean-Pierre Merlet. The (true) Stewart Plarform has 12 configurations[C]. IEEE Conf. on Robotic and Automation. 1994,3:2160-2165.
[8] Carlo Innocenti, Vincenzo Parenti-Castelli, Direct Position Analysis of the Stewart Platform[J]. Mech. Mach. Theory. 1990, 25(6):611-621.
[9] Chang-de Zhang, Shi-Min Song. Forward Kinematics of a Class of Parallel (Stewart) Platform with Closed-Form Solutions[C]. Proceedings of the 1991 IEEE, International Conference on Robotics and Automation, Sacrameto, California. April 1991:2676-2681.
[10] S.V. Screenivasan, K.J. Waldron, P. Nanua. Colsed Form Direct Displacement Analysis of a 6-6 Stewart Platform. Mech[J]. Mach. Theory 1994, 29(6):855-864.
[11] J.-P. Merlet. Direct Kinematics and Assembly Modes of Parallel manipulators[J]. The International Jouranl of Robotics Research. 1992,8(5):150-162.
[12] Charles W. Wampler. Forward Displacement Analysis of General Six-In-Parallel SPS(stewart) Platform manipulators Using Soma Coordinates[J]. Mech. Mach. Theory 1996, 33(3):331-337.
[13] M. Raghavan. The Stewart platform of General Geometry Has 40 Configurations[J]. Transction of the ASME Journal of Mechanical Design. JUNE 1993, 115:277-282.
[14]姜虹,贾嵘,董宏智.六自由度并联机器人位置正解的数值解法[J].上海交通大学学报,2000,34(3):351-353.
[15] K.H. Hunt, E.F.J. Primrose. Assembly configurations of Some In-Parallel-Actuated Manipulators[J]. Mech.Mach.Theory 1993,28(1)31-42P.
[16]黄真,孔宪文.6-SPS并联机器人机构运动分析[J].东北重型机械学院学报,1997,16(2):189-194页.
[17]李鹭扬,吴洪涛.并联机构正运动学一维搜索法[J],扬州大学学报(自然科学版),2000,3(1):66-70.
[18]吴军,李铁民,关立文.飞行模拟器运动平台的计算力矩控制[J],清华大学学报(自然科学版,2006,46(8)1405-1408.
[19]吴东苏,顾宏斌.飞行模拟器运动平台关节空间自适应模糊控制[J],南京航空航天大学学报,2008,40(5)670-676.
[20]吴东苏,顾宏斌.轻型飞行模拟器运动平台自适应模糊反步控制[J],仪器仪表学报,2008,29(7),1464-1469.
[21]丁东风.飞行模拟器仪表的计算机图形实现[J],计算机应用,1993,6,41-43.
[1]王行仁.飞行实时仿真系统及技术[M].北京:北京航空航天大学出版社,1998.
[2]陈绍祖.林克飞行模拟器[J].模拟技术,1989(4):24.
[3]李学国.飞行训练模拟器的发展[J].国际航空,1989(4):45-47.
[4] Allen L D. Evolution of Flight Simulator[M]. AIAA-93-354-CP,1993.
[5]梁恺.航空仿真技术[J].测控技术,1998(3):8-10.
[6]章晨. 90年代飞行模拟器的发展动向[J].国际航空. 1992,(7):10-11.
[7] Daniel Lazard, Jean-Pierre Merlet. The (true) Stewart Plarform has 12 configurations[C]. IEEE Conf. on Robotic and Automation. 1994,3:2160-2165.
[8] Carlo Innocenti, Vincenzo Parenti-Castelli, Direct Position Analysis of the Stewart Platform[J]. Mech. Mach. Theory. 1990, 25(6):611-621.
[9] Chang-de Zhang, Shi-Min Song. Forward Kinematics of a Class of Parallel (Stewart) Platform with Closed-Form Solutions[C]. Proceedings of the 1991 IEEE, International Conference on Robotics and Automation, Sacrameto, California. April 1991:2676-2681.
[10] S.V. Screenivasan, K.J. Waldron, P. Nanua. Colsed Form Direct Displacement Analysis of a 6-6 Stewart Platform. Mech[J]. Mach. Theory 1994, 29(6):855-864.
[11] J.-P. Merlet. Direct Kinematics and Assembly Modes of Parallel manipulators[J]. The International Jouranl of Robotics Research. 1992,8(5):150-162.
[12] Charles W. Wampler. Forward Displacement Analysis of General Six-In-Parallel SPS(stewart) Platform manipulators Using Soma Coordinates[J]. Mech. Mach. Theory 1996, 33(3):331-337.
[13] M. Raghavan. The Stewart platform of General Geometry Has 40 Configurations[J]. Transction of the ASME Journal of Mechanical Design. JUNE 1993, 115:277-282.
[14]姜虹,贾嵘,董宏智.六自由度并联机器人位置正解的数值解法[J].上海交通大学学报,2000,34(3):351-353.
[15] K.H. Hunt, E.F.J. Primrose. Assembly configurations of Some In-Parallel-Actuated Manipulators[J]. Mech.Mach.Theory 1993,28(1)31-42P.
[16]黄真,孔宪文.6-SPS并联机器人机构运动分析[J].东北重型机械学院学报,1997,16(2):189-194页.
[17]李鹭扬,吴洪涛.并联机构正运动学一维搜索法[J],扬州大学学报(自然科学版),2000,3(1):66-70.
[18]吴军,李铁民,关立文.飞行模拟器运动平台的计算力矩控制[J],清华大学学报(自然科学版,2006,46(8)1405-1408.
[19]吴东苏,顾宏斌.飞行模拟器运动平台关节空间自适应模糊控制[J],南京航空航天大学学报,2008,40(5)670-676.
[20]吴东苏,顾宏斌.轻型飞行模拟器运动平台自适应模糊反步控制[J],仪器仪表学报,2008,29(7),1464-1469.
[21]丁东风.飞行模拟器仪表的计算机图形实现[J],计算机应用,1993,6,41-43.
[39] Maniezzo V, Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem[D]. Techniacl Report CSR 98-1, Scienze dell Informazione, Universita di Bologna, Sede di Cesena, Italy, 1998.
[40] Maniezzo V, Colorni A. The ant system applied to the quadratic assignment problem[J]. IEEE Transactions on Knowledge and Data Engineering, 1999, 11(5):769-778.
[41] Colorni A, Dorigo M, Maniezzo, et al, Ant system for job-shop scheduling[J]. Belgian J. Oper. Res. Statist. Comput. Sci. 1994, 34:39-53.
[42] Stutzle T. An ant approach to the flow shop problem[D]. Technical Teport AIDA-97-07, FG Intellektik, FB Informatik, TH Darmstadt, September 1997.
[43] Besten M den, Stutzle T, Dorigo M. Scheduling Single machines by ants[D]. Technical Report IRIDIA/9916, IRIDIA, Universite Libre de Bruxelles, Belgium,1999.
[44] Bullnheimer B, Hartl R G, Strauss C. An improved ant system algorithm for the vehicle routing problem[D]. Technical Report POM-10/97, Institute of Management Science, University of Vienna, Austria, 1997, Ann. Oper.Res. 89,1999.
[45] Bullnheimer B, Hartl R F, Strauss C. Applying the ant system to the vehicle routing problem[C]. Metaheuristics:Advances and Trends in Local Search Paradingms for Optimization, Kluwer Academic Publishers, Boston, MA, 1999, 285-296.
[46] Gambardella L M, Taillard E, Agazzi G. MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows[J]. New Ideas in Optimization, McGraw-Hill, London, UK, 1999, 63-76.
[47] Gambardella L M, Dorigo M, HAS-SOP: a hybrid ant system for the sequential ordering problem[D]. Technical Report IDSIA-11-97, IDSIA, Lugano, Switzerland.1997.
[48] Costa D, Hertz A. Ants can colour graphs[J]. Journal of the Operational Research Society, 1997, 48(3):295-305.
[49] Ahn S H, Lee S G, Chung T C, Modified ant colony system for coloring graphs[C]. Proceedings of the 2003 Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and the 4th Pacific Rim Conference on Mutimedia, 2003,3,1849-1853.
[50] Ramalhinho Lourenco H, Serra D. Adaptive approach heuristics for the generalized assignment problem[D]. Technical Report EWP Series No.304, Department of Economics and Management, Universitat Pompeu Fabra, Barcelona,1998.
[51] Leguizamon G, Michalewicz Z. A new version of ant system for subset problems[C]. Proceedings of the 1999 Congress on Evolutionary Computation, 1999, 1459-1464.
[52] Parra-Hernandez R, Dimopoulos N. On the performance of the ant colony system for solving the multidimensional knapsack problem[C]. Proceedings of the 2003 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2003,1:338-341.
[53] Liang Y C, Smith A E. An ant system approach to redundancy allocation[C]. Proceedings of the 1999 Congress on Evolutionary Computation, 1999, 2:1478-1484.
[54] Liang Y C, Smith A E. An ant colony optimization algorithm for the redundancy allocation problem(RAP)[J]. IEEE Transactions on Reliability, 2004, 53(3):417-423.
[55] Bauer A, Bullnheimer B, Hartle R F, et al. An ant colony optimization approach for single machine total tardiness problem[C]. Proceedings of the 1999 Congress on Evolutionary Computation, 1999, 1445-1450.
[56] Chang C S, Tian L, Wen F S, New approach to fault section estimation in power systems using ant system[J]. Electric Power Systems Research, 1999,49(1):63-70.
[57]孙京诰,李秋艳,杨欣斌等.基于蚁群算法的故障识别[J].华东理工大学学报,2004,30(2):194-198.
[58] Schoofs L, Naudts B. Ant coonies are good at solving constraint satisfaction problems[C]. Proceedings of the 2000 Congress on Evolutionary Computation,2000,2:1190-1195.
[59] Solnon C. Ant can solve constraint satisfaction problems[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(4):347-357.
[60] Fu S G, Dozier G. Solving distributed constraint satisfaction problems with an ant-like society of hillclimbers[C]. Proceedings of the International Conference on Artificial Intelligence, 2003,1:263-369.
[61] Mertens K, Holvoer T,CSAA: a distributed ant algorithm framework for constraint satisfaction[C]. Proceedings of the 17th International Florida Artificial Intelligence Research Society Conference, 2004, 2:764-769.
[62] Mathur M,Karale S B, Priye S,et al. Ant colony approach to continuous function optimization[J]. Industrial and Engineering Chemistry Research, 2000,39(10):3814-3822.
[63]魏平,熊伟清.用于一般函数优化的蚁群算法[J].宁波大学学报,2001,14(4):52-55.
[64] Abbaspour K C, Schulin R, Van Genuchten M Th. Estimating unsaturated soil hydraulic parameters using ant colony optimization[J]. Advances in Water Resources, 2001,24(8) :827-841.
[65]汪镭,吴启迪.蚁群算法在系统识别中的应用[J].自动化学报,2003,29(1):102-109.
[66] Duan H B, Wang D B, Zhu J Q, et al. Parameter identification of LuGre friction model for flight simulation servo system based on ant colony algorithm[J]. Transactions of Nanjing University of Aeronautics&Astronautics,2004,21(3):179-183.
[67] Parpinelli R S, Lopes H S, Freitas A A. Data mining with an ant colony optimization algorithm[J]. IEEE Transactions on Evolutionary Computation, 2002,6(4):321-332.
[68] Meshoul S, Batouche M. Ant colony system with extremal dynamics for point matching and pose estimation[C]. Proceeding of the 16th International Conference on Pattern Recogintion, 2002, 3:823-826.
[69] Zheng G, Wong A, Nahavandi S. Hybrid ant colony algorithm for texture classification[C]. Proceeding of the 2003 Congress on Evolutionary Computation, 2003, 4:2648-2652.
[70] Kumar V, Sahin F. Foraging in ant colonies applied to the mine detection problem[C]. Proceedings of the IEEE International Workshop on Soft Computing in Industral Applications, 2003, 6.
[71] Kumar V, Sahin F. Foraging in ant colonies applied to the mine detection problem[C]. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, 6:5429-5434.
[72] Chapman E, Sahin F. Application of swarm intelligence to the mine detection problem[C]. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, 6:5429-5434.
[73] Shmygelska A, Aguine-Hemundez R, Hoos H H. An ant colony algorithm for the 2D HP protein folding problem[C]. Proceedings of the 3rd International Workshop on Ant Algotithms/ANTS2002, Lecture Notes in Computer Science, 2002, 2463:40-52.
[74] Li Y M, Xu Z B, An ant colony optimization heuristic for solving maximum independent set problems[C]. Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications, 2003, 206-211.
[75] Korosec P, Silc J, Robic B. Solving the mesh-partitioning problem with an ant-colony algorithm[J]. Parallel Computing, 2004, 30(5-6):785-801.
[76]曹春红,卢奕南,李文辉.改进的蚂蚁算法在几何约束求解中的应用[J].工程图学学报,2003,25(4):46-50
[77]曹春红,李文辉,张永坚.遗传蚂蚁算法在几何约束求解中的应用[J].仪器仪表学报,2003,25(4):393-396
[78]高尚,杨静宇,吴小俊.圆排列问题的蚁群模拟退火算法[J].系统工程理论与实践,2004,(8):102-106
[79] Jensen R, Shen Q, Fuzzy-rough data reduction with ant colony optimization[J]. Fuzzy Sets and Systems, 2005, 149(1):5-20.
[80]段海滨,蚁群算法及其在高性能电动仿真转台参数优化中的应用研究[D],南京:南京航空航天大学博士学位论文,2005.
[81]何靖华.面向仿生制造的蚂蚁算法分析研究及应用[D],武汉:华中科技大学硕士学位论文,2002.
[82] Holland JH. Adaptation in natural and artificial systems [D]. Ann Arbor: University of Michigan Press; 1975.
[83] Konak A., D.W. Coit, and A.E. Smith. Multi-objective optimization using genetic algorithms: A tutorial [J]. Reliability Engineering and System Safety, 2006, 91(9): 992-1007.
[84] S.Kirkpatrick, C.D.Gelatt, and M.P.Vecchi, Optimization by Simulated Annealing[J], Science 220,1983:637-680.
[85] Garey.M.R. nd D.S.Johnson, Computers and Intractability: a guide to the theory of NP-completeness[J]. Freeman, San Francisco, 1979.
[86] M.Lundy and A.Mess, Convergence of an Annealing Algorithm[J], Mathematical programming, Vol 34,1986:111-124.
[87] A.Casotto, F.Romeo, and A.Sangiov anni-Vincetelli, A parallel Simulated Anneling Algorithm for Placement of Macro-Cells[C], IEEE Trans, Computer-Aided Design,Vol.6,No.5,1987:837-847.
[88] Hao Chen,Nicholas S.Flann and Daniel W.Watson, Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Algorithm, Parallel and Distributed System, Vol.9 No.2,Feb.1998:126-136.
[89]何友,王国宏,陆大金.多传感器信息融合及应用[M],电子工业出版社2000.
[90]孙全.多传感器信息融合与多目标跟踪系统的研究[D],浙江大学博士学位论文,2000.
[91] Moose R L. An adaptive state estimation solution to the maneuvering target problem[C]. IEEE Trans. On Automatic Control, 1975, 20(6):359-362.
[92] Kifubarajan T, Bar-Shalom Y, Pattipai K R, Kadar I. Ground target tracking with variable structure IMM estimator[C]. IEEE Trans. On Aerospace and Electronic Systems. 2000, 36(1):26-46.
[93]周宏仁,敬忠良,王陪德.机动目标跟踪[M],北京:国防工业出版社,1991:10-21,219-233
[94] Sittler R W. An optimal data association problem in surveillance theory[C]. IEEE Military Electronics, 1964, 8(2):125-139.
[95] Kifubarajan T, Bar-Shalom Y, Pattipai K R, Kadar I. Ground target tracking with variable structure IMM estimator[C]. IEEE Trans. On Aerospace and Electronic Systems. 2000, 36(1):26-46.
[96] Jikkov V P, Angelova D S, Sernerdije T A. Design and comparidon of mode-set adaptive IMM algorithms for maneuvering target tracking[C]. IEEE Trans. On Aerospace and Electronic Systems. 1999, 35(1):343-350.
[97] Balckman S S. Multiple Hypothesis tracking for multiple target tracking[J]. Aerospace and Electronic Systems.2004, 19(1):5-18.
[98] Zhang X, Willett P, Bar-Shalom Y. Uniform versus nonuniform sampling when tracking in clutter[J]. Aerospace and Electronic Systems. 2006, 42(2):388-400.
[99] Stubberud S C, Kramer K A. Data association for multiple sensor types using fuzzy logic[C]. IEEE Trans. On Instrumentation and Measurement. 2006, 55(6):2292-2303.
[100] Long-Huai Wang, Hsueh-Jyh Li. Using range profiles for data association in multiple-target tracking[C]. IEEE Trans. On Aerospace and Electronic Systems. 1996,32(1) :444-450.
[101] Challa S, Ba-Ngu Vo, Xuezhi Wang. Bayesian Approaches to track existence to track existence-IPDA and random sets[C]. 5th International Conference on Information fusion, 2002, 2:1228-1235.
[102] Ba-Ngu Vo, Singh S. On the Bayes filtering equations of finite set statistics[C]. Proc. 5th Asian Control Conference, 2004, 2:1264-1269.
[103] Vermaak J, Godsill S J, Perez P. Monte Carlo filtering for multi-target tracking and data association[C]. IEEE Trans on Aerospace and Electronic Systems. 2005, 41(1):309-332.
[104] Miguez J, Artes-Rodriguez A. A Monte Carlo method for joint node location and maneuvering target tracking in a sensor netwoerk.[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, 2006, 4:989-922.
[105] Soohl Jeong, Tugnait J K. Multisensor tracking of a maneuvering target in clutter using IMMPDA filter with simultaneous measurement update[C]. IEEE Trans. On Aerospace and Electronic. 2005, 41(3):1122-1131.
[106] Megherbi N, Ambellouis S, Colot O, Cabestaing F. Data association in multi-target tracking using belief theory: handling target emergence and disappearance issue[C]. IEEE Conference on Advanced Video and Signal Based Surveillance, Como, Italy, 2005:517-521.
[107] Taek Lyul Song, Dong Gwan Lee. A probabilistic nearest neighbor filter algorithm for validated measurements[C]. IEEE Trans. On Signal Processing, 2006, 54(7):2797-2802.
[108] Michael Kalandros, Lucy Y Pao. Multisensor covariance control strategies for reducing bias effects in interacting target scenarios[C]. IEEE Trans. On Aerospace and Electronic Systems. 2005, 41(1):153-173.
[109] Huimin Chen, X Rong Li, Yaakov Bar-Shalom. On joint track initiation and parameter estimation under measurement origin uncertainty[C]. IEEE Trans. On Aerospace and Electronic Systems, 2004, 40(2):675-694.
[110] Matt L Milier, Harold S Stone, Ingemer J Cox. Optimizing Murty’s ranked assignment method[C]. IEEE Trans. On Aerospace and Electronic Systems.1997, 33(3):851-862.
[111]汪圣利.跟踪密集多回波环境下多机动目标的IMM-PC算法[J],自动化技术与应用,2004,23(7):1-3.
[112] REID D. An algorithm for tracking multiple target [J]. IEEE Trans on Automat and Contr , 1979 ,24 (6) :84 - 90.
[113] SAMUEL BLACKMAN, ROBER POPOLI, Design and Analysis of Modern Tracking Systems [M]. Boston :Artech House ,1999.
[114] DEB S, YEDDANAPUDI M, PATTIPATI K R. A generalized S-D assignment algorithm for multisensor-multitarget state estimation [J]. IEEE Trans on AES, 1997,33 (2) :523 - 538.
[115] DEB S, PATTIPATI K R, BAR-SHALOM Y, A multisensor-multitarget dataassociation algorithm for heterogeneous sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(2):560-568.
[116] BLAIR W R, WASTON G A, KIRUBARAJAN T. Benchmark for radar allocation and tracking in ECM [J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4):1097-1114.
[117] KIRUBARAJAN T, BAR-SHALOM Y, BLAIR W R. IMMPSAF for radar management and tracking benchmark with ECM [J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4):1115-1133.
[118] DAEIPOUR E, BAR-SHALOM Y, IMM tracking of maneuvering targets in the presence of glint [J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(3):996-1003.
[119]康莉,谢维信,黄敬雄.一种基于蚁群算法的多目标跟踪数据关联方法[J] .电子学报,2008,36 (3) : 586-589.
[120]朱力立,张焕春,经亚枝.一种基于模糊自适应GA的广义S维分配算法[J].模式识别与人工智能,2004 ,17( 1):82-86.
[121]胡炜薇,赵坤,杨莘元.基于粒子群算法的广义S维分配算法[J].导弹与制导学报,2005, 25(4):791-795.
[122]肖宏峰,谭冠政.基于遗传算法的混合蚁群算法[J].计算机工程与应用, 2008, 44(16), 42-45.
[123]刘波,杨路明,雷刚越.融合粒子群与蚁群算法优化XML群体智能搜索[J],计算机研究与发展, 2008, 45(8), 1371-1378.
[124] Konak A.,D.W. Coit and A.E. Smith. Multi-objective optimization using genetic algorithms: A tutorial [J]. Reliability Engineering and System Safety, 2006, 91(9): 992-1007.
[125] Roth J P, Bouricius W G, Schneider P R. Programmed algorithms to computer tests to detect and distinguish between failures in logic circuits[J]. IEEE Transactions on Electronic Computers, 1967, EC-16:567-580
[126]杨士元.数字系统的故障诊断与可靠性设计[M].北京:清华大学出版社,2000.39-49.
[127] Goel P. An implicit enumeration algorithm to generation test for combinational circuits[J]. IEEE Transactions on Computers. 1981, C-30(3):215-222.
[128] Fujiwara H. FAN: a fanout-oriented test pattern generation algorithm[C]. 1985 international Symposium on Circuits and Systems, Kyoto, Japan, 1985, 2:671-674.
[129] Schulz M H, Trischler E, Sarfert T M, SOCRATES: A highly efficient automatic test pattern generation system[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 1988,7(1):126-136.
[130] Gizdarski E, Fujiwara H, SPIRIT: A highly robust combinational test generation algorithm[J]. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems. 2002,21(12):1446-1458.
[131] Tafertshofer P, Ganz A, Henftling M. A sat-based implication enging for efficient ATPG, equivalence checking, and optimization of netlists[C]. Proceedings of the International Conference on Computer-Aided Design, 1997:648-655.
[132] Hayashi T, Kita H, Hatayama K. A genetic approach to test generation for logic circuits[C]. Proceedings of the Third Asian Test Symposium, Nara, Japan, 1994:101-106.
[133] Long Wangning, Yang Shiyuan, Min Yinghua, et al. Level-oriented GA-based test generation of logic circuits[C]. Proceedings of the IEEE International Conference on Intelligent Processing Systems, Beijing, China, 1998, 1:563-567.
[134]潘中良,张光昭.数字电路多故障测试生成的神经网络方法研究[J].仪器仪表学报,1999,20(3):232-234.
[135]刘小东,孙圣和.基于神经网络的组合电路测试生成算法[J].哈尔滨工业大学学报, 2002, 4:255-257.
[136]徐建斌,李智.神经网络在组合电路故障模拟测试生成算法中的应用[J].电路与系统学报,2001,6(4):109-110.
[137]候艳丽.数字集成电路测试生成算法研究[D],哈尔滨工业大学,博士学位论文,2008.
[138] W.B. Jone and C. A. Papachristou, Acoordinate Circuit Partitioning and Test Generation Method for Pseudo-Exhaustive Testing of VLSI Circuits[J], IEEE Trans. On CAD, vol.14, no.3, 374-384P, Mar, 1995.
[139] K. T. Cheng, C. J. Lin, Timing-Driven Test Point Insertion for Full-Scan and Partial-Scan BIST[J], Proc. Of International Test Conf., 506-514P, 1995,10.
[140] E. J. McCluskey, Verification Testing-A Pseudoexhaustive Test Technique[J], IEEE Trans. On Computers, vol.C-33, no.6, 541-546P,1984,06.
[141] E. J. McCluskey, S. Bozorgui-Nesbat, Design for Autonomous Test[J], IEEE Trans. On Computers vol.C-30, no.11, 866-875P,1981,11.
[142] M.W. Roberts and P.K. Lala, An Algorithm for the Patritioning of Logic Circuits[J], IEEE Proc., vol.131,pt.E,no.4,1984,07.
[143] R.Srinivasan, S.K. Gupta and M.A. Breuer, An Efficient Partitioning Strategy for Pseudo-Exhaustive Testing[J], Proc. Of Design Automation Conf., 242-248, 1993,06.
[144] S.C. Chang and J.C. Rau, An Timing Driven Pseudo Exhaustive Testing for VLSI Circuits[C]. Departmenti of Computer Scient and Information Engineering National Chung-Cheng University Chiayi, Taiwan, R.O.C.
[145] Gizdarski E, Fujiwara H. SPIRIT: a highly robust combinational test generation algorithm[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2002, 21(12):1446-1459.
[146] Hamzaoglu I, Patel J H. New techniques for deterministic test pattern generation[J]. Jouranl of Electronic Testing: Theory and Applications. 1999,15:63-73.
[147] Saab D G, Saab Y G, Abraham J A. CRIS: A test cultivation program for sequential VLSI circuits[J]. 1992 IEEE/ACM International Conference on Computer-Aided Design, Santa Clara, AC, USA, 1992:216-219.
[148]朱虎,宋恩民,路志宏.求解图着色问题的最大最小蚁群搜索算法[J].计算机仿真,2010,27(3):190-192.
[149]廖飞雄,马良.图着色问题的启发式搜索蚂蚁算法[J].计算机工程,2007,33(16):191-195.
[150] Jinno C, Inoue M, Fujiwara H. Internally balanced structrue with hold and switching functions[J]. Transactions of the Institute of Electronics, Information and Communication Engineers D-I. 2003, J86D-I(9):682-690.
[151] Fujiwara H. The complexity of fault detection problems for combinational logic circuits[J]. Transactions of the Information Processing Society of Japan. 1986, 27(8):768-773.
[152] Hamzaoglu I, Patel J H. Test set compaction algorithms for combinational circuits[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2000, 19(8):957-963.
[153] Lu Changhua, Zhang Qibo, Jiang Weiwei. A global optimization algorithm for the minimum test set of circuits[C]. IEEE International Conference on Robotics, Intelligent Systems and Signal Proceeding, Changsha, Hunan, China, 2003, 2:1203-1207.
[154] Sanchez E, Reorda M S, Squillero G. Efficient techniques for automatic verification-oriented test set optimization[J]. International Journal of Parallel Programming. 2006, 34(1):93-109.
[155] Qiao Jia-Qing, Fu Ping, Yin Hong-Tao. Test set optimization based on genetic reordering[J]. Tieh Tzu Hsueh Pao. 2007,35(12):2335-2338.
[156] Santoso Y, Merten M, Rudnick E M, et al. FreezeFrame: Compact test generation using a frozen clock strategy[J]. Design, Automation and Test in Europe Conference and Exhibition, Munich, Germany, 1999:747-752.
[157] Dimopoulos M, Linardis P. Efficient static compaction of test sequences sets through the application of set covering techniques[J]. Proceedings of Design, Automation and Test in Europe Conference and Exhibition, Paris, France, 2004:194-199.
[158] Islam S Z, Ali M A M. Test pattern optimization using proper seed selection in mixed-mode technique[C]. Proceedings of Third IEEE International Workshop on Electronic Design, Test and Application, Kuala Lumpur, Malaysia, 2006:105-109.
[159] Fukunaga M, Kajihara S, Xiaoqing Wen, et al. A dynamic test compaction procedure for high-qualit path delay testing[C]. Proceedings of Asia and south Pacific Design Automation Conference, Yokohama, Japan, 2006:348-353.
[160] Hsiao M S, Rudnick E M, Patel J H. Fast static compaction algorithm for sequential circuit test vectors[J]. IEEE Transactions on Computers. 1999, 48(3):188-195.
[161]杨士元.数字系统的故障诊断与可靠性设计[M].北京:清华大学出版社,2000.94– 102.
[162] Pomeranz I, Reddy S M. On the compaction of test sets produced by genetic optimization[J]. Proceedings of Sixth Asian Text Symposum, Akita, Japan, 1997:4-9.
[163] Bossen D C,HongSJ.eause-effect analysis for multiple fault detection in combinational networks[J]. IEEE trans.OnComPuter.11.1971.
[164] Agrawa1P,Agrawal V D.Probabilistic analysis of random test generation method for irredundant combinational logic network[J]. IEEETrans.ComPut.1972, C-21.
[165] W.B.Jone and C.A.PaPaehristou.A Coordinate Cireuit Partitioning and Test Generation Method for Pseudo-Exhaustive Testing of VLSI Circuits[J]. IEEE Trans.on CAD.vol.14,no.3,374-384, Mar.1995.
[166]俞龙江,彭喜源,彭宇.基于蚁群算法的测试集优化[J].电子学报,2003,31(8) :1178 - 1181.
[167]王小港.遗传算法在VLSI设计自动化中的应用研究[D] . 2001.
[168]乔家庆,付平,尹洪涛.基于遗传排序的测试集优化[J]电子学报, 2007, 35 (12) : 2335 - 2338.