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: 17201 |
Author:wxd |
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Description: 粒子群优化算法（PSO）是一种进化计算技术（evolutionary computation）,由Eberhart博士和kennedy博士于1995年提出 (Kennedy J，Eberhart R．
Particle swarm optimization．Proceedings of the IEEE International Conference on Neural Networks．1995．1942~1948.)。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．
-Particle Swarm Optimization (PSO) is an evolutionary computation techniques (evolutionary co mputation) by Dr. Eberhart and kennedy Dr. raised in 1995 (Kennedy, J., Eberhart R. Particle swarm optimization.Proc eedings of the IEEE International Conference o n Neural Networks.1995.1942 ~ 1948.) . From the flock of the predatory behavior. PSO algorithm is the basic idea of individual groups through the sharing of information and collaboration to find the optimal solution. Platform: |
Size: 134585 |
Author:周荷 |
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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: 16633 |
Author:张正 |
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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（PSO）是一种进化计算技术（evolutionary computation）,由Eberhart博士和kennedy博士于1995年提出 (Kennedy J，Eberhart R．
Particle swarm optimization．Proceedings of the IEEE International Conference on Neural Networks．1995．1942~1948.)。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．
-Particle Swarm Optimization (PSO) is an evolutionary computation techniques (evolutionary co mputation) by Dr. Eberhart and kennedy Dr. raised in 1995 (Kennedy, J., Eberhart R. Particle swarm optimization.Proc eedings of the IEEE International Conference o n Neural Networks.1995.1942 ~ 1948.) . From the flock of the predatory behavior. PSO algorithm is the basic idea of individual groups through the sharing of information and collaboration to find the optimal solution. Platform: |
Size: 134144 |
Author:周荷 |
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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: |
Size: 8192 |
Author:娟娟 |
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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: 多变量输入、输出、多干扰、非线性和强耦合的复杂系统控制是一个比较困难的问题，常用的控制器可能因为多变量耦合问题难以控制系统。PID神经元网络是一种多层前向神经元网络，它的各层神经元个数、连接方式、连接权值是按照PID控制规律的已有原则和经验确定的，是一种动态的符合控制系统的前向网络。但是由于PID网络初始权值随机取值的原因，每次控制的效果都有所差别，个别情况下控制效果还比较差。本案例研究了基于PID神经元的多变量耦合系统控制，并用PSO算法来优化控制器以取得更好的控制效果。-Multivariable input, output, and more interference, complex nonlinear and strong coupling system control is a more difficult problem, commonly used multivariable controller may be because the problem is difficult to control the coupling system. PID neural network is a multi-front to neural networks, its number of neurons in each layer, connection, connection weights are in accordance with the existing principles and experience to determine the PID control law is a dynamic compliance before the control system to the network. However, due to the random initial weights of the network PID values of reason, every time there are differences in the effect of control and in some cases the control effect is still relatively poor. This case study of multivariable coupling system based on PID control neurons, and with PSO algorithm to optimize the controller to achieve better control effect. Platform: |
Size: 14336 |
Author:wujiewen |
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Description: 一本介绍Matlab应用的书籍，包括神经网络、SVM、智能寻优PSO等等，书里包括了应用实例及相应的代码，对于matlab编程初学者非常有用-An introduction to the application of Matlab books, including neural networks, SVM, intelligent search PSO and so on, the book includes the application examples and the corresponding code, very useful for matlab programming beginners Platform: |
Size: 5805056 |
Author:吴俊 |
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Description: 应用小区域方差对比，程序简单，包括最小二乘法、SVM、神经网络、1_k近邻法,包含了阵列信号处理的常见算法，可以广泛的应用于数据预测及数据分析,基于分段非线性权重值的Pso算法，通过反复训练模板能有较高的识别率。- Application of small area variance comparison, simple procedures, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Contains a common array signal processing algorithm, Can be widely used in data analysis and forecast data, Based on piecewise nonlinear weight value Pso algorithm, Through repeated training SDAoJNglate have higher recognition rate. Platform: |
Size: 5120 |
Author:ajawzf |
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Description: PID控制器的性能取决于Kp、Ki、Kd这3个参数是否合理，因此，优化PID控制器参数具有重要意义。目前，PID控制器参数主要是人工调整，这种方法不仅费时，而且不能保证获得最佳的性能。PSO已经广泛应用于函数优化、神经网络训练、模式分类、模糊系统控制以及其它应用领域，本案例将使用PSO进行PID控制器参数的优化设计。-Performance of PID controller depends on the Kp, Ki, Kd these three parameters are reasonable, therefore, to optimize PID controller parameters is important. Currently, PID controller parameters are mainly manual adjustment, this method is not only time consuming, but can not guarantee optimum performance. PSO has been widely used function optimization, neural networks, pattern classification, fuzzy systems control and other applications, in this case using the PSO is optimized PID controller parameters. Platform: |
Size: 1024 |
Author:任凡 |
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Description: 粒子群优化算法PSO是可以用于各种神经网络的优化，然后可以进行分类。包含各种粒子群函数，matlab编写的。-PSO PSO is used to optimize a variety of neural networks, and can be classified. Particle swarm contains various functions, matlab prepared. Platform: |
Size: 8192 |
Author:筱玉 |
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Description: In this paper three different methods such as GDM
AGDM and modified PSO are adopted to optimize the neural
networks, and simulate the DTC system with
MATLAB/SIMULINK. The result shows: modified PSO
algorithm can solve the problem of neural networks better. It
increases the convergence rate, enhance the precision, and avoid the local minimum Platform: |
Size: 149504 |
Author:samir |
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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: 用于pso等各种优化算法以及各种交叉使用的算法 神经网络还有单纯型法的最大最小优化算法(Various optimization algorithms, such as PSO, and all kinds of intersecting algorithm neural networks and the simplex optimization algorithm of simplex method) Platform: |
Size: 1024 |
Author:hxxa |
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Description: BP神经网络容易陷于局部极小值，PSO算法在无约束非线性函数优化方面性能优越，通常可以直接找寻到全局最优解，即使不能搜多到全局最优解，也距离全局最优点不远。当然，基本PSO算法陷入局部极值也是有的。对于这个缺点目前还没有找到比较有效、省市的解决方案。本案例实现利用PSO算法和BP算法共同训练神经网络，先将网络进行PSO算法训练，然后BP算法接着进行小范围精细搜索，PSO算法训练神经网络的本质就是将输出误差函数（即能量函数）看成目标函数，PSO对能量函数进行全局寻找最小值。(BP neural networks are prone to local minimum values. The PS algorithm has superior performance in the optimization of unconstrained nonlinear functions. It can usually find the global optimal solution directly. Even if it can not find more global optimal solutions, it is not far from the global best. Of course, there is also a local extreme of the basic PO algorithm. For this shortcoming, there is no more effective, provincial and municipal solution. This case realizes the use of the SO algorithm and the BP algorithm to train the neural network. First, the network is trained in the SO algorithm, and then the BP algorithm is followed by a small range of fine searches. The essence of the PO algorithm training neural network is to regard the output error function(ie, the energy function) as the objective function, and the PO seeks a global minimum value for the energy function.) Platform: |
Size: 3072 |
Author:Katri |
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Search - pso and neural networks