Description: 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-comparison with the genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications Platform: |
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Author:wxd |
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Description: 粒子群优化算法！！！
系统地介绍了粒子群优化算法,归纳了其发展过程中的各种改进如惯性权重!收敛因子!跟踪并
优化动态目标等模型\"阐述了算法在目标函数优化!神经网络训练!模糊控制系统等基本领域的应用并
给出其在工程领域的应用进展,最后,对粒子群优化算法的研究和应用进行了总结和展望,指出其在计算
机辅助工艺规划领域的应用前景\"-PSO algorithm! ! ! A systematic introduction to PSO algorithm, summed up its development process such as the improvement of inertia weight! Convergence factor! track and dynamic optimization model objectives, "explained the algorithm optimization objective function! Neural Network Training! Fuzzy Control System basic areas of application and gives the project from its the application domain, finally, the PSO algorithm research and application of the summary and outlook. pointed out in the field of computer-aided process planning applications prospects " Platform: |
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Author:八云 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications Platform: |
Size: 16384 |
Author: |
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Description: 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-comparison with the genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications Platform: |
Size: 16384 |
Author:wxd |
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Description: 粒子群优化算法！！！
系统地介绍了粒子群优化算法,归纳了其发展过程中的各种改进如惯性权重!收敛因子!跟踪并
优化动态目标等模型"阐述了算法在目标函数优化!神经网络训练!模糊控制系统等基本领域的应用并
给出其在工程领域的应用进展,最后,对粒子群优化算法的研究和应用进行了总结和展望,指出其在计算
机辅助工艺规划领域的应用前景"-PSO algorithm! ! ! A systematic introduction to PSO algorithm, summed up its development process such as the improvement of inertia weight! Convergence factor! track and dynamic optimization model objectives, "explained the algorithm optimization objective function! Neural Network Training! Fuzzy Control System basic areas of application and gives the project from its the application domain, finally, the PSO algorithm research and application of the summary and outlook. pointed out in the field of computer-aided process planning applications prospects " Platform: |
Size: 53248 |
Author:八云 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究
PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍
同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications Platform: |
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Author:zzh |
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Description: 用隶属函数型神经网与模糊控制融合的解耦程序-Membership functions with neural networks and fuzzy control integration of the decoupling procedure Platform: |
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Author:娟娟 |
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Description: 微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。-Particle Swarm Optimization [PSO] are equal by Kennedy and Eberhart in 1995 developed an evolutionary computing technology, from preying on the birds of the simulation process. PSO with genetic algorithm is similar to an iterative optimization-based tool, but the use of genetic algorithms and genetic manipulation of different sub-optimize the use of groups between the various entities within the " collaboration" and " competitive" relationship, according to themselves and their competition the flying experience, adjust their behavior. Comparison with genetic algorithms, PSO has the advantage of being simple and easy and did not realize the need to adjust the parameters much. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications. Platform: |
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Author:wzy |
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Description: This M-file is about using Particle Swarm Algorithm (PSO) to train a Fuzzy Neural Network. Platform: |
Size: 1024 |
Author:Mehran Ahmadlou |
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Description: 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域，基于Java语言实现。-Compared with the genetic algorithm, PSO has the advantage is simple and easy and there is no need to adjust many parameters. Has been widely applied to function optimization, neural network training, fuzzy system control and other applications of genetic algorithms, based on Java language. Platform: |
Size: 2048 |
Author:吴帅 |
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Description: PSO算法简单、易实现且参数较少，现已被应用于函数优化、神经网络训练、模糊系统控制以及其它遗传算法的应用领域-PSO algorithm is simple, easy to implement and less parameters, have been applied to function optimization, neural network training, fuzzy system control and other applications of genetic algorithms Platform: |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究。
PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。
同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。
-Particle swarm optimization (PSO) is an evolutionary computing (evolutionary computation), there is invented by Dr. Eberhart and Dr. kennedy. From the behavior of birds of prey. PSO with genetic algorithm is similar to an iteration-based optimization tool. System is initialized to a group of random solutions, the optimal value by iterative search. But there is no genetic algorithm with the cross (crossover) and mutation (mutation). But the particles in the solution space to follow the optimal particle search. Comparison with genetic algorithms, PSO has the advantage of simple and easy to implement and there is no need to adjust many parameters. Has been widely used in function optimization, neural network training, fuzzy system control, and other genetic algorithm applications. Platform: |
Size: 10240 |
Author:天涯 |
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Description: 本书共有30个MATLAB神经网络的案例,包括BP、RBF、SVM、SOM、Hopfield、LVQ、Elman、小波等神经网络,还包含PSO(粒子群)、灰色神经网络、模糊网络、概率神经网络、遗传算法优化等内容。-The book consists of 30 MATLAB neural network case (including program can be run), including BP, RBF, SVM, SOM, Hopfield, LVQ, Elman, wavelet neural networks, etc. also contains the PSO (PSO), gray neural networks, fuzzy networks, probabilistic neural networks, genetic algorithm optimization and so on. Platform: |
Size: 28765184 |
Author:万二 |
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Description: MATLAB 神经网络源代码，包括BP网络、RBF网络、模糊神经网络、小波神经网络、PSO群优化等共43个网络源代码，并有实际案例-MATLAB neural network source code, including BP network, RBF network, fuzzy neural network, wavelet neural network, PSO Swarm Optimization, a total of 43 network source code, and there are actual cases Platform: |
Size: 25954304 |
Author:yishuang |
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Description: 该PDF共有30个MATLAB神经网络的案例，包括BP、RBF、SVM、SOM、Hopfield、LVQ、Elman、小波等神经网络；还包含PSO(粒子群)、灰色神经网络、模糊网络、概率神经网络、遗传算法优化等内容。本PDF作为本科毕业设计、研究生项日设计、博士低年级课题设计参考书籍，同时对广大科研人员也有很高的参考价值。(The PDF has a total of 30 MATLAB in the case of neural networks, including BP, RBF, SVM, SOM, Hopfield, LVQ, Elman and wavelet neural network; also contains PSO (Li Ziqun), grey neural network, fuzzy neural network, probabilistic neural network, genetic algorithm etc.. The PDF as a graduate design, graduate design and doctoral low grade project design reference books, but also for the vast number of scientific research personnel also have a high reference value.) Platform: |
Size: 47037440 |
Author:那又怎样
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Description: 粒子群优化算法(PSO：Particle swarm optimization) 是一种进化计算技术（evolutionary computation）。
源于对鸟群捕食的行为研究。粒子群优化算法的基本思想：是通过群体中个体之间的协作和信息共享来寻找最优解．
PSO的优势：在于简单容易实现并且没有许多参数的调节。目前已被广泛应用于函数优化、神经网络训练、模糊系统控制以及其他遗传算法的应用领域。(The particle swarm optimization (PSO:Particle swarm optimization) is an evolutionary computing technology (Evolutionary Computation).
The study of the behavior of bird predator. The basic idea of particle swarm optimization (PSO) is to find the optimal solution through the collaboration and information sharing among the individuals in the group.
The advantage of PSO is that it is simple and easy to implement without many parameters. At present, it has been widely used in function optimization, neural network training, fuzzy system control and other genetic algorithms.) Platform: |
Size: 5120 |
Author:安联的大球童 |
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Description: 粒子群优化（PSO）是一种进化计算技术（进化计算）。
捕食鸟行为的研究。粒子群算法（PSO）的基本思想是通过群体中个体之间的协作和信息共享找到最优解。
粒子群优化算法的优点是它简单且易于实现，没有多个参数。目前，它已广泛应用于函数优化、神经网络训练、模糊系统控制等遗传算法中。(The particle swarm optimization (PSO:Particle swarm optimization) is an evolutionary computing technology (Evolutionary Computation).
The study of the behavior of bird predator. The basic idea of particle swarm optimization (PSO) is to find the optimal solution through the collaboration and information sharing among the individuals in the group.
The advantage of PSO is that it is simple and easy to implement without many parameters. At present, it has been widely used in function optimization, neural network training, fuzzy system control and other genetic algorithms.) Platform: |
Size: 238592 |
Author:安联的大球童 |
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