用于神经网络分析与设计

蚁群算法解决0-1背包问题,是一个比较小的程序，运行过，没有问题的。-the solving method of zero to one backage with ACO.It is a small programe for the zero to one problem.

神经网络的源代码，关于字符识别的一些东西-Neural network source code, something about character recognition

递归神经网络故障诊断，可以看看，不错的哦-Recurrent neural network fault diagnosis, you can see, oh well

k中心点 编制和调试一个程序，它将用户输入的正规式转换为以状态图和矩阵形式表示的确定有穷自动机。 1．把正规式转换为NFA 2．将NFA确定化为DFA • ＃作为正规式的终止符 • 考虑复合正规式 • 开始状态号为0 -focal point for the preparation of k and debug a program, it will the user to enter the formal conversion to a state diagram and matrix forms express the determination of DFA. 1. The formal type is converted to NFA2. Will determine the NFA into a DFA

SVM的讲义！具体的内容可以自己看！很详细的-SVM lectures! Specific content can look! Very detailed

遗传算法解决双变量的函数最优化问题，有按钮的界面，用bc所编，生动模拟遗传进化过程-genetic algorithm to solve the two- variable optimization function, the button interface, using bc prepared by the vivid simulation of the process of genetic evolution

two Matlab functions for initializing and training a recurrent neural network -two Matlab functions for initializing and training a recurrent neural network

遗传算法演示程序-GA Demonstration Program

HOPFIELD的一个经典用例，解决TSP问题，代码完整，是我自己编写的哦-HOPFILED cases with a classic, TSP solution, code integrity, I was prepared oh

4。《演化程序――遗传算法和数据编码的结合》，[英]米凯利维兹着 科学出版社 2000年第一版 本书分三个部分共16章分别介绍了:1.遗传算法的概念、数学原理及方法步骤 2.遗传算法和数据编码联系起来所构成的演化程序 3.演化程序面向一些实际问题的应用。 本书语言生动，结构合理，较少使用专业性术语和深涩词汇，适合面临优化问题的研究生、程序员、设计师、工程师及科研工作人员参考。-4. "Evolutionary process-- genetic coding algorithm and data integration," [E] Mikhail Liweici a 520-531 2000 version of the first book in three parts a total of 16 chapters were introduced : 1. Genetic Algorithm concept mathematical principles and methods of Step 2. genetic algorithms and data coding linked posed by the evolution of procedures 3. Evolution-oriented procedures some of the practical problems of application. The book vivid language, reasonable structure, and less use of professional terminology and vocabulary deep acerbic suitable for optimization problems facing graduate students, programmers, designers, engineers and research personnel.