本人调试的大作业（源程序模型+论文），主要包括PID控制、模糊控制、神经网络控制、遗传算法优化神经网络控制（使用了遗传工具箱GAOT）对同一系统所作的仿真比较，

一套从入门到精通的Matlab学习教材，附有大量例子，包括神经网络源代码-a proficient from entry to the study and teaching of Matlab, with a large number of examples, including source code neural network

计算机模式识别技术-the technology of the computer pattern recognation

用Delphi语言编写的遗传算法，内容较多，可以参考-Delphi language prepared by the genetic algorithm, a great deal of content, they can refer to

著名的神经网络工具箱，功能十分强大，里面包含了数十种神经网络算法的实例。-Well-known neural network toolbox, function very strong, which contains dozens of examples of neural network algorithm.

基本遗传算法 里面的函数可自行更换 适合初学者 容易理解-A function of the basic genetic algorithm which can be suitable for beginners is easy to understand on their own replacement

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.

一种基于子波特征的模糊神经网络目标识别方法，有一定参考意义-based on wavelet characteristics of the FNN target identification methods, a certain reference value

c 神经网络遗传算法-c neural network genetic algorithm

This a GA implementation using binary and real coded variables. Mixed variables can be used. Constraints can also be handled. All constraints must be greater-than-equal-to type (g >= 0) and normalized (see the sample problem in prob1 in objective()).-This a GA implementation using binary and real coded variables. Mixed variables can be used. Constraints can also be handled. All constraints must be greater-than-equal-to type (g gt;= 0) and normalized (see the sample problem in prob1 in objective()).

英国svm编写的环境用vc++开发，有完整的源代码，一定会有帮助！