人工神经网络算法研究及应用 田景文.包括神经网络、遗传算法、小波网络、模拟退火算法和支持向量机，清晰易懂，非常值得一看。...

1、该工具包用于安装Lm和RBF两个神经网络工具包 2、直接运行LmNet.exe或_install.bat即可； 3、完成成功后，请将 binwin32 目前加至系统的 path中。 具体为：我的电脑-->属性-->环境变量-->将“binwin32”加至path路径中。-1, the tool kits for the installation and Lm 2 RBF neural network tool kits 2, or directly running LmNet.exe _install.bat can; Three complete success, I would be grateful if bin win32 increase to the current system which the path. Specifically : my computer-- gt; Attribute-- gt; Environment variables-- gt; "Bin win32" to increase path PATH.

一个利用遗传算法解决TSP问题的程序,有利于对遗传算法进一步的理解.-A use of genetic algorithms to solve TSP problems procedure is beneficial to further understanding of genetic algorithm.

如何在VB下实现BP神经网络的拓扑优化算法

通过人工神经网络实现异或问题的解决，用visual c++开发-through artificial neural network or differences in the solution of problems with the development of visual c

遗传算法,实现的是一个猜答案的过程， 这种算法比较经典，可以借鉴-genetic algorithm to achieve guess the answer is a process, this method is classic, we can draw on

这是我自己做的改进的遗传算法，主要是克服遗传算法的早熟问题-This is what I want done to improve the genetic algorithm, which is aimed at overcoming the genetic algorithm early problems

神经网络控制作为一种全新的智能控制方法,是解决非线性系统预测和控制问题的一种重要手段,受到了许多专家学者的广泛关注,是目前国内外研究的热点之一。本文着重研究前向神经网络的学习算法,简单探讨了BP算法在逼近非线性系统中各个因素对系统的影响。 -Neural Network Control as a new intelligent control method is to solve the nonlinear system to predict and control an important means by many experts and scholars in a wide range of concerns, is one of the hot research. This article focuses on feedforward neural network learning algorithm, a simple BP algorithm of approximation of nonlinear systems in the various factors on the system.

本算法为自适应遗传算法，采用基于排序的选择策略，编码方案为实数编码。能够解决很多优化问题！-The algorithm for the adaptive genetic algorithm, based on the sort of choice strategy, coding schemes for real-coded. Many optimization problems can be solved!

遗传算法的经典之作-Genetic Algorithm for the classics

本程序是人工编写的bp神经网络，针对识别样本btw三类进行训练和识别。-This procedure is prepared bp artificial neural networks, btw samples for identification of three types of training and identification.