Description: 用神经网络和遗传算法解决tsp问题，先使用神经网络训练出较优个体，然后再进化，可以反其道而行，-using neural networks and genetic algorithms to solve the problem tsp first use of neural network training of better individual, then evolution can be just the opposite. Platform: |
Size: 161574 |
Author:问涛 |
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Description: 用神经网络和遗传算法解决tsp问题，先使用神经网络训练出较优个体，然后再进化，可以反其道而行，-using neural networks and genetic algorithms to solve the problem tsp first use of neural network training of better individual, then evolution can be just the opposite. Platform: |
Size: 160768 |
Author:问涛 |
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Description: 人工神经网络实现的扫雷机程序,用遗传算法进化网络的权重效果很好-Artificial neural network to achieve the de-mining machine procedures, the evolution of the network by using genetic algorithms the weight well Platform: |
Size: 2086912 |
Author:lihongda |
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Description: 基于MATLAB的差分进化算法优化神经网络的源程序-MATLAB based on the differential evolution algorithm for optimization of neural network source code Platform: |
Size: 1024 |
Author:胡月 |
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Description: 利用遗传算法对两个神经网络模块共同进化的例子，此方法优于对两个模块分别进化-Using the genetic algorithm neural network module of the two examples of co-evolution, this method is superior to the evolution of the two modules were Platform: |
Size: 10240 |
Author:tjy |
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Description: 人工神经网络(Artificial Neural Network)是从生理角度对智能的模拟，具有极
高的学习能力和自适应能力，能够以任意精度逼近任意函数，完成对系统的仿真；
而遗传算法是对自然界生物进化过程的模拟，具有极强的全局寻优能力，这两种
算法都是当下研究较多的智能方法。将这两种方法与常规的 PID 控制相结合，
构成智能 PID 控制器，使其具有参数自整定、自适应的能力，以适应复杂环境
下的控制要求，这一思路对提高控制效果具有很好的现实意义。
-Artificial Neural Network (ANN) is an imitation of the intelligence by the point of physiological. It has a high capacity of learning and adaptive, can approximate any function to arbitrary accuracy, and complete the simulation of the system. The Genetic
algorithm is a simulation of natural biological evolution, which has a strong ability of
global optimization. These two algorithms are more intelligent method of current
research. The idea of combining these two methods with the conventional PID
controller to be a intelligent controller with the abilities of parameter auto-tuning and
adaptive for the requirements of the complex environment, has a high practical
significance of improving the control effect.
Platform: |
Size: 661504 |
Author:baijiaxuan |
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Description: 介绍了神经网络实现遗传算法模型。
作者J.C.Astor & C.Adami-This artical main discus the model for the evolution using a method of Artificial Neural Network. Platform: |
Size: 515072 |
Author:戴晓天 |
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Description: 遗传算法优化神经网络GA_NN.C语言,同步进化BP网络的拓扑结构和连接权值，避免了网络结构的选择和设计,非常实用。-Genetic algorithm optimization neural network GA_NN.C language, synchronization topology and evolution BP network connection weights, avoiding the selection and design of the network structure, very practical. Platform: |
Size: 291840 |
Author:sigoo |
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Description:
A concept for the optimization of nonlinear functions using particle swarm methodology is introduced.
The evolution of several paradigms is outlined, and an implementation of one of the paradigms is
discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear
function optimization and neural network training, are proposed. The relationships between particle
swarm optimization and both artificial life and genetic algorithms are described, Platform: |
Size: 2226176 |
Author:kajal |
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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: |
Size: 10104832 |
Author:alvin |
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Description: This code use a Differential Evolution optimization algorithm.
and then optimize the learning rates of neural network.
used is JENKIN s gasro and makey dataset.
-This code use a Differential Evolution optimization algorithm.
and then optimize the learning rates of neural network.
used is JENKIN s gasro and makey dataset.
Platform: |
Size: 26624 |
Author:packchanjun |
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Description: 利用差分进化算法对数据进行全局搜索，然后结合神经网络做预测-Using differential evolution algorithm to global search data, and then combined with the neural network prediction Platform: |
Size: 60416 |
Author:wujin |
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Description: 该文件是差分进化算法在神经网络中的应用分析，算法使用不同的样本点经过多次实验证明该算法有效。-This file is in the Application of differential evolution algorithm neural network algorithm using different sample points after many experiments show that the algorithm is effective. Platform: |
Size: 15360 |
Author:杨鹏 |
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Description: 差分进化方法是一种新的智能算法，可以和多种算法结合，例如神经网络优化；约束性算法；线性算法。-Differential evolution method is a new intelligent algorithm, and can be combined with a variety of algorithms, such as neural network optimization constraint algorithm linear algorithm. Platform: |
Size: 1024 |
Author:wangming |
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Description: 基于MATLAB的差分进化算法优化神经网络的源程序-MATLAB based on the differential evolution algorithm for optimization of neural network source code Platform: |
Size: 1024 |
Author:刘曦 |
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Description: 优化算法有很多，经典算法包括：有线性规划，动态规划等；改进型局部搜索算法包括爬山法，最速下降法等，模拟退火、遗传算法以及禁忌搜索称作指导性搜索法。而神经网络，混沌搜索则属于系统动态演化方法。
梯度为基础的传统优化算法具有较高的计算效率、较强的可靠性、比较成熟等优点，是一类最重要的、应用最广泛的优化算法。但是，传统的最优化方法在应用于复杂、困难的优化问题时有较大的局限性。(There are many optimization algorithms, the classical algorithms include linear programming, dynamic programming, etc. the improved local search algorithms include hill-climbing method, steepest descent method, etc. simulated annealing, genetic algorithm and tabu search are called the guiding search methods. The neural network and chaotic search belong to the dynamic evolution method of the system.
Gradient based traditional optimization algorithm has the advantages of high computational efficiency, strong reliability and relatively mature. It is one of the most important and most widely used optimization algorithms. However, the traditional optimization method has great limitations when it is applied to complex and difficult optimization problems.) Platform: |
Size: 1857536 |
Author:韬文
|
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Search - evolution neural network