Description: 本文基于遗传算法思想，采用浮点数矩阵表示编码，在遗传算法的进化过程中加入一定的约束条件等方法，探讨了网络结构的设计和学习。经实例分析，在用于建立大坝安全监控预报模型的前馈神经网络设计中，该方法在满足一定约束条件下，能同时有效地寻找合适的网络结构和相应的参数(神经网络的权值和阈值)，且在精度和速度上都有较大的提高，为实现实时在线分析评价大坝的安全性态提供了有力的技术支持。-Based on the genetic algorithm, using a float matrix coding, Genetic algorithms in the evolutionary process to be bound by certain conditions, to explore the structure of the network design and learning. By analyzing the examples used in the establishment of dam safety monitoring forecasting model of neural network design, The constraint in meeting certain conditions, can effectively find suitable network structure and the corresponding parameters (the neural network weights and thresholds), and the accuracy and speed have improved greatly. To achieve real-time online analysis and evaluation of the safety of the dam states provide strong technical support. Platform: |
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Author:汪顺和 |
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Description: 本文基于遗传算法思想，采用浮点数矩阵表示编码，在遗传算法的进化过程中加入一定的约束条件等方法，探讨了网络结构的设计和学习。经实例分析，在用于建立大坝安全监控预报模型的前馈神经网络设计中，该方法在满足一定约束条件下，能同时有效地寻找合适的网络结构和相应的参数(神经网络的权值和阈值)，且在精度和速度上都有较大的提高，为实现实时在线分析评价大坝的安全性态提供了有力的技术支持。-Based on the genetic algorithm, using a float matrix coding, Genetic algorithms in the evolutionary process to be bound by certain conditions, to explore the structure of the network design and learning. By analyzing the examples used in the establishment of dam safety monitoring forecasting model of neural network design, The constraint in meeting certain conditions, can effectively find suitable network structure and the corresponding parameters (the neural network weights and thresholds), and the accuracy and speed have improved greatly. To achieve real-time online analysis and evaluation of the safety of the dam states provide strong technical support. Platform: |
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Author:汪顺和 |
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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: 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: 传算法的基本原理、设计方法及其并行实现，以及它在组合优化、机器学习、图像处理、过程控制、进化神经网络-Propagation algorithm of the basic principles, design methods and their parallel implementation, as well as in combinatorial optimization, machine learning, image processing, process control, evolutionary neural network Platform: |
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Author:鲁明 |
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Description: ：遗传算法是一种典型的进化算法。文中分析了遗传算法的特点和神经网络的特点，从而得出了把两种算法结合
起来进行应用的思想。运用理论对比的方法，阐明了用遗传算法进行神经网络性能优化的原因，并得出结论，认为用遗传
算法进行神经网络性能优化促使了神经网络更进一步的应用。阐述了遗传算法优化神经网络的两种主要方法，论述了遗
传算法和神经网络的发展现状和将来的研究动向。-: Genetic algorithm is a typical evolutionary algorithm. This paper analyzes the characteristics of genetic algorithms and neural network features, to arrive at a combination of the two algorithms applied thinking. Method of comparing the use of theory to clarify the genetic algorithm using neural network performance optimization reasons, and concluded that the neural network using genetic algorithm neural network performance optimization to promote further applications. Genetic algorithm described two main methods of neural networks, discusses the genetic algorithm and neural network development status and future research trends. Platform: |
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Author:dengxiaoxu |
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Description: 微粒群优化算法(Particle Swarm Optimization,PSO算法)源于鸟群和鱼群群体运
动行为的研究，是一种新的群体智能优化算法，是演化计算领域中的一个新的分支。它
的主要特点是原理简单、参数少、收敛速度较快，所需领域知识少。该算法的出现引起
了学者们极大的关注，已在函数优化、神经网络训练、组合优化、机器人路径规划等领
域获得了广泛应用，并取得了较好的效果。尽管粒子群优化算法发展近十年，但无论是
理论分析还是实践应用都尚未成熟，有大量的问题值得研究。
-Particle swarm optimization (Particle Swarm Optimization, PSO algorithm) from groups of birds and fish movement behavior, is a new swarm intelligence algorithm, in the field of evolutionary computation is a new branch. Its main feature is simple in principle, few parameters, convergence is faster, less domain knowledge required. The algorithm brought the scholars are of great concern, has been in function optimization, neural network training, combinatorial optimization, robot path planning has been widely used applications, and achieved good results. Despite the development of particle swarm optimization nearly a decade, but both theory and practice applications are not yet mature, a large number of issues worth studying. Platform: |
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Author:赵 |
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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: Multi-step-prediction of chaotic time series based
on co-evolutionary recurrent neural network
协同进化递归神经网络的多步混沌时间序列预测-This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic
time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent
neural networks by co-evolutionary strategy. The searching space was separated into two subspaces and the individuals
are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability
of recurrent neural network to incorporate past experience due to internal recurrence. The eff ectiveness of CERNN is
evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey–Glass series and real-world
sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic
time series. Platform: |
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Description: 进化神经网络学习实例很全很好用，初学者很实用-Evolutionary neural network learning example is the whole very good, and very useful for beginners Platform: |
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Author:ysj |
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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: |
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Author:xox |
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Description: 计算智能是以模型（计算模型、数学模型）为基础、以分布并行计算为特征的模拟人的智能求解问题的理论与方法，本书系统的讲述计算智能的基本内容基本理论与基本方法1、从模拟智能的生成过程的观点讲述模拟进化的计算理论。2、从模拟智能结构的观点讲述人工神经网络理论。3、从模拟智能行为的观点讲述模糊逻辑与模糊推理。全书突出基础，强调背景，跟着研究与发展-Computational Intelligence based model (model, mathematical model)-based, distributed simulation of parallel computing is characterized by human intelligence theory and methods for solving problems, this book tells the system the basic content of Computational Intelligence basic theories and methods 1, generation process from analogue intelligent simulated evolutionary point of view about the computational theory. 2, from the point of view about the smart structures simulated artificial neural network theory. 3, from the point of view about the intelligent behavior analog fuzzy logic and fuzzy reasoning. The book is highlighted, emphasizing the background, followed by research and development Platform: |
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Description: 全书包括两部分共15章。第1章指出问题求解困难的原因；第2章简要介绍几本概念；第3-5章分别综述穷举搜索法、局部搜索法、贪婪法、分而治之法、动态规划法、分支定界法、模拟退火法和禁忌搜索法；第6-7章介绍一般演化算法的细节问题；第8-10章介绍如何采用演化方法求解TSP问题、处理约束条件以及算法调整；第11章讨论了环境和噪声问题；第12-13分别提供神经网络和模糊系统相关内容；第14章对混合系统和扩展演化算法做简短讨论；第15章介绍演化算法在实际问题中的应用，并给出有价值的提示。-The book includes two parts with 15 chapters. The first chapter points out the reasons for the problem solving difficult the second chapter briefly introduces some of the concepts Chapter 3-5 respectively in exhaustive search, local search, greedy, divide and rule method, dynamic programming, branch and bound method, simulated annealing and tabu search method 6-7 chapter details the general evolution algorithm how to 8-10 chapter introduces evolution method for solving TSP problem, constraint conditions and algorithm the eleventh chapter discusses the environment and the problem of noise the 12-13 respectively with neural network and fuzzy system related content the fourteenth chapter on the mixing system and evolution algorithm is briefly discussed the fifteenth chapter introduces the application of evolutionary algorithm in practical problems, and gives valuable hints. Platform: |
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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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Description: 用神经网络，α-β剪枝以及进化算法实现五道棋程序的智能。Java编写，没有添加界面部分，仅仅是控制台上的程序。-Neural network, α-β pruning and evolutionary algorithms five Dao intelligent program. Written in Java, do not add the interface part, merely procedural console. Platform: |
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Search - Evolutionary neural network