遗传算法与工程设计

Matlab神经网络工具箱应用简介 神经网络已经在各个领域中应用，以实现各种复杂的功能。这些领域包括：模式识别、 鉴定、分类、语音、翻译和控制系统。 如今神经网络能够用来解决常规计算机和人难以解决的问题。我们主要通过这个工具箱 来建立示范的神经网络系统，并应用到工程、金融和其他实际项目中去。-Matlab neural network toolbox application profile neural network has been applied in various fields in order to achieve a variety of complex functions. These areas include: pattern recognition, identification, classification, voice, translation and control systems. Today neural networks can be used to conventional computers and people to solve difficult problems. We mainly through the toolbox to create a model of neural network systems, and applied to the engineering, financial and other practical projects.

神经网络用于牌照识别-neural network license for identification

本算法使用了遗传算法的方法实现了随机数的生成方法。能够比较平均得实现随机数的生成。-The algorithm uses a genetic algorithm to achieve the method of random number generator. Compared to the average achieved in the generation of random numbers.

在vc上运行的回答集求解器源代码，能够供在人工智能上学习的学习者使用-Running in vc answer set solver source code, for use in artificial intelligence to learn the use of the learners

人工免疫网络,实现分类,聚类,函数寻优等功能,采用matlab编写.-artificial immune network, classification, clustering, search function Excellence function, prepared using Matlab.

使用Astar算法实现的八数码问题。C++编写，控制台应用程序，实现简单，代码轻便。-Astar algorithm using eight digital issues. C++ written in the console application, to achieve simple and lightweight code.

对多类点进行分类，有图像界面，使用了多种分类算法-Points to the many types of classification, there are graphical interfaces, using a variety of classification algorithms

这是一个用MATLAB软件写的遗传算法程序。-This is a software written using MATLAB genetic algorithm procedure.

HMM的C语言实现.很有用的模式识别程序-HMM of the C language. Very useful pattern recognition procedures

8。《遗传算法及其应用》，陈国良等编着 人民邮电出版社 2001年第一版 本书系统全面地介绍了遗传算法的基本原理、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、模糊模式识别和人工生命等方面的应用。 本书可作为高等院校计算机、无线电电子学、自动控制、生物医学工程等有关专业高年级学生或研究生的教材和参考书，也可供从事人工智能、信息处理研究和应用的科技人员学习参考。-8. "Genetic algorithm and its application," Chen Guoliang edited and published with the People"s Post and in 2001 the first book version of the full introduction of the genetic algorithm basic tenets , design methods and parallel implementation, and its portfolio optimization, machine learning, image processing, process control, evolutionary neural networks, Fuzzy Pattern Recognition and Artificial Life, and so on the application. The book can be used as institutions of higher learning computers, radio electronics, automatic control, biomedical engineering, and other professional grade students or graduate teaching and reference materials, are also available in artificial intelligence, information processing and application of information technology to study.