Kohonen the SOFM (self

人工神经网络BP算法 1、动态改变学习速率 2、加入动量项 3、运用了Matcom4.5的矩阵运算库（可免费下载,头文件matlib.h)， 方便矩阵运算,当然，也可自己写矩阵类 4、可暂停运算 5、可将网络以文件的形式保存、恢复-Artificial neural network BP algorithm 1, the dynamic change learning rate 2, adding momentum of 3, using the matrix calculation Matcom4.5 library (can be downloaded free of charge, the first document matlib.h), facilitate the matrix calculation, of course, can also write their own matrix Class 4, may be suspended by the operator 5, may be the form of network to the preservation, restoration

人工智能AI经典入门书，很难找的噢，给喜欢的朋友-classical AI Artificial Intelligence portal, it is very difficult to find the, uh, likes to friends

很好的分类程序。。。。C++语言编写。。。VC开发环境-good classification procedure. . . . C language. . . VC development environment.

这是好用的源代码-This is the source code handy

一个用Java编写的分形程序。分形(Fractal)理论是Mandelbrot于20世纪70年代提出的一门非线性学科，用于描述自然界中的非平衡非线性复杂系统的有序状态。利用分形算法可以画出千奇百怪的图象。Mandelbrot集和Julia集是分形集合中最基本的两个集合-Using Java to prepare a fractal process. Fractal (Fractal) theory is the Mandelbrot in the 20th century, 70s raised a non-linear science, used to describe the nature of the non-equilibrium nonlinear complex systems in an orderly state. Using fractal algorithm can draw strange image. Mandelbrot set and Julia fractal sets is the most basic collection of two collections

基于VC开发的神经网络工具箱，能进行各种神经网络的开发与设计(annie1.5)-development of the VC-based neural network toolbox that can be used for a variety of neural network development and design (annie1.5)

Schapire 所著的Boosting算法的教程。 对目前物体识别和机器学习领域中广泛应用的Boosting算法展开了深入浅出的描述，还有很多Toy Examples。-Tutorial for Boosting algorithm by Schapire

基于信息熵的免疫算法，解决TSP问题的，但是我也是初学，这个看不太明白，共享一下吧-Entropy-based immune algorithm to solve TSP problem, but I am also a beginner, do not quite understand this view, share yourself

汉字识别系统，非常简单，网络上看到的 一起学学啊，对模式的一个简单匹配.-Chinese character recognition system is very simple, see the Web together to learn ah, the model of a simple matching.

模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S―R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S "S" = S+ sign (). delta where given for the step, [-1,1] Random Number