Description: PSO算法具有快速收敛而且有很强的跳出局部最优从而找到全局最优点的能力，故可以用它来训练优化神经网络，此程序主要研究这个方面。-PSO algorithm is fast and has a strong convergence of jumping out of the local optimal thus find the most advantages of the overall capacity, it can be used to train the neural network optimization, this procedure major study this aspect. Platform: |
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Author:lt |
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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: ：针对移动机器人传统路径规划算法效率不高，寻优能力差等问题，提出一种基
于神经网络和粒子群优化算法相结合的移动机器人路径规划方法．该方法利用神经网
络实现大量的并行和分布计算，发挥PSO简单、容易实现的优点，提高了路径规划的计
算效率和可靠性．仿真结果表明，这种新路径规划方法是可行且有效的．-The quality and eficiency of calculation is the two puzzling problems in the tradi—
tional algorithm for the robot path planning．In this paper，a new method of obstacle avoidance
and path planning based on neural network and particle swarm optimization is proposed．In
this method，a neural network is used to realize substantive parallel and distributed compu—
ting．And also this exerts the merit of PSO，which improves the computational eficiency and
reliability．As it is proved by analysis and test，that a better result is obtained by the pro—
posed algorithm． Platform: |
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Author:王风 |
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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: |
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Author:天涯 |
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Description: MATLAB神经网络30个案例分析__读者调用案例的时候，只要把案例中的数据换成自己需要处理的数据，即可实现自己想要的网络。该书共有30个MATLAB神经网络的案例(含可运行程序），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。-30 case studies of the MATLAB Neural Network __ readers call the case, as long as the data in the case replaced by the data they need to be addressed, you can achieve your desired network. A total of 30 MATLAB neural network case (including running the program) in the book, including BP, RBF, SVM, the SOM, Hopfield, on LVQ, Elman, wavelet neural network also contains the PSO (Particle Swarm), gray neural network, fuzzy networks, probabilistic neural networks, genetic algorithms optimization. Platform: |
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Author:liuwei |
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Description: 用PSO算法优化神经网络，全面，并且有数据，这是我我的毕业设计，得到了老师的认可，希望得到大家的认可。
-PSO algorithm optimization neural network, comprehensive, and data, this is my graduation project, has been recognized by the teacher, hoping to get everyone' s approval. Platform: |
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Author:hundanpl |
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Description: ＢＰ神经网络具有较强的非线性问题处理能力 是目前一 种 较 好 的 用 于 时 间 序 列 预 测 的 方 法 然 而 它 存 在 易 于 陷 入 局 部 极 小,针对地震预测的应用,用改进粒子群优化的ＢＰ算法对四川地区最大震级时间序列进行预测,通过训练 预 测 次 年 的 最 大 震 级 结 果,表明此方法优于未经优化的 ＢＰ算法具有良好的预测效果 -BP neural network has a strong nonlinear problems processing power is a method for time series prediction, however it is easy to fall into local minimum, the application for earthquake prediction, BP algorithm with improved particle swarm optimization Sichuan maximum magnitude time series prediction by training the forecast for the following year the results of the maximum magnitude, indicating that this method is better than the non-optimized BP algorithm has good predictive Platform: |
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Author:mali |
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Description: The Wavelet Neural Network
This is simple example for using of modified Morlet neural network.
Levenberg-Marquardt with numerical Jacobian calculation implemented.
Easy to use with other optimization algorithem e.g GA,PSO, etc.
The function "xox_lm" may be useful for other optimization problem easily.
The function "xox_jacobian" may be useful for Jacobian calculation easily.
The function "NN_fcn" may be used by the evolutionary based neural network.
The activation function "modified Morlet" will be replaceable by others.- The Wavelet Neural Network
This is simple example for using of modified Morlet neural network.
Levenberg-Marquardt with numerical Jacobian calculation implemented.
Easy to use with other optimization algorithem e.g GA,PSO, etc.
The function "xox_lm" may be useful for other optimization problem easily.
The function "xox_jacobian" may be useful for Jacobian calculation easily.
The function "NN_fcn" may be used by the evolutionary based neural network.
The activation function "modified Morlet" will be replaceable by others. Platform: |
Size: 2048 |
Author:xox |
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Description: 粒子群优化算法是一类基于群智能的随机优化算法。因受
到人工生命的研究结果启发，
&’((’)*
和
+,’-./-0
1 ，!2
于
334
年
提出了粒子群优化算法，并已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他的应用领域。-PSO is a kind of swarm intelligence-based stochastic optimization algorithms. Inspired by the study results due to artificial life, & ' ((' )* and+, ' -./-0 1 ,! 2 334 made in PSO, and has been widely used in function optimization, neural network training, pattern classification, fuzzy systems control and other applications. Platform: |
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Author:kobe |
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Description: 粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来发展起来的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法。
BP（Back Propagation）神经网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hidden layer)和输出层(output layer)。-Particle swarm optimization, also known as particle swarm optimization (Particle Swarm Optimization), abbreviated as PSO, is a new evolutionary algorithm developed in recent years (Evolutionary Algorithm- EA). Kind, and simulated annealing algorithm PSO algorithm is similar evolutionary algorithms, it is also starting a random solution, through an iterative search for the optimal solution, which is also used to uate the quality through fitness solution, but it is simpler than genetic algorithm rules It has no genetic algorithm " crossover" (Crossover) and " variant" (Mutation) operation, which by following the current search to find the optimal value to the global optimum. This algorithm is its easy implementation, high accuracy, fast convergence, etc. attracted academic attention and show its superiority in solving practical problems. PSO algorithm is a parallel algorithm. BP (Back Propagation) neural network is a 1986 team of scientists headed by Rumelhart and McC Platform: |
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Author:艾岳巍 |
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Description: 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 basic idea of Particle Swarm Optimization (PSO) is to find the optimal solution by cooperating and sharing information among individuals.
The advantage of PSO is that it is simple and easy to implement and does not have many parameters to adjust. Has been widely used in function optimization, neural network training, fuzzy system control and other genetic algorithm applications. Platform: |
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Author:华盛顿 |
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Description: 用粒子群算法PSO优化BP神经网络，改善预测精度(The BP neural network is optimized by particle swarm optimization (PSO) to improve the prediction accuracy) Platform: |
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Author:yjabbt
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Description: 粒子群算法优化BP神经网络，可用于指标预测(BP neural network optimized by Particle swarm optimization (PSO) that can be used for index prediction) Platform: |
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Author:霜月 |
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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: |
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Author:Katri |
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Description: 1.批量最小二乘法算法（也称最小二乘的一次性完成辨识算法）
2.递推最小二乘法算法，应用递推算法对参数估计值进行不断修正,以取得更为准确的参数估计值。
3.粒子群算法(PSO)。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的优点在于简单容易实现并且没有许多参数的调节。
4.BP神经网络，各个神经元仅接收来自前一级的输出，经神经元处理后的信息将输出至下一级，网络中没有反馈，即前一级神经元不会接受后一级神经元的输出。
water tank是原始数据（双容水箱实验）(1. Batch least squares algorithm (also known as least squares one-time completion identification algorithm)
2. Recursive least squares algorithm, applying the recursive algorithm to continuously modify the parameter estimates to obtain more accurate parameter estimates.
3. Particle Swarm Optimization (PSO). The basic idea of particle swarm optimization algorithm is to find the optimal solution through the cooperation and information sharing between individuals in the group. The advantage of PSO is that it is simple and easy to implement and does not have many parameters to adjust.
4.BP neural network, each neuron only receives the output from the previous level, and the information processed by the neuron will be output to the next level. There is no feedback in the network, that is, the neurons in the previous level will not receive the neurons in the next level Meta's output.
water tank is the original data (double capacity water tank experiment)) Platform: |
Size: 6144 |
Author:系基金迪欧 |
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Description: 粒子群算法优化的BP神经网络与传统BP神经网络对比(Comparison between BP neural network optimized by particle swarm optimization and traditional BP neural network) Platform: |
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Author:xinlingxiaosui |
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Search - optimization neural network by pso