神经网络lvq算法程序

开发环境:Matlab 简要说明:自组织特征映射模型(Self-Organizing feature Map)，认为一个神经网络接受外界输入模式时，将会分为不同的区域，各区域对输入模式具有不同的响应特征，同时这一过程是自动完成的。各神经元的连接权值具有一定的分布。最邻近的神经元互相刺激，而较远的神经元则相互抑制，更远一些的则具有较弱的刺激作用。自组织特征映射法是一种无教师的聚类方法。-development environment : Matlab Brief Description : Self-Organizing Map model (Self-Organizing Map feature), a neural network that external input mode, will be divided into different regions, the regional input to the model with different response characteristics and the process is automatic End %. The neurons connect with the right to a certain value of the distribution. Most neighboring neurons stimulate each other, distant neurons were mutual inhibition, the vision has a weaker stimulus. Self-organizing feature mapping method is a non-teachers clustering method.

是实现关系型贝叶斯网络一中机器学习算法，从国外相关研究人员的主页上下载收集。

多重网格法在step形状区域上的实现,三种不同光滑步可以实现.-Multi-Grid Method in the step shape of the region on the realization of three different smoothing step can be achieved.

用隶属函数型神经网与模糊控制融合的解耦程序-Membership functions with neural networks and fuzzy control integration of the decoupling procedure

matlab编写的BP神经网络算法，的一个应用小程序-matlab prepared by BP neural network algorithm, a small process applications

用感知器分类算法和最近邻分类算法对三类点进行分类，具有窗口界面，用鼠标点击就可以了，程序自动给出分类界面-Classification algorithm with the sensor and the nearest neighbor classification algorithm to classify three categories of points, with the window interface, with a mouse click can be had, the program automatically gives Classification Interface

本人做的遗传算法源代码，在FORTRAN下打开即可以，可在WINDOUS环境下运行-I do genetic algorithm source code, under the FORTRAN that can be opened, in WINDOUS operating environment

用parzen窗方法，估计概率密度，采用高期核函数-With parzen window means of estimating the probability density function using high nucleus. . . .

该程序提供了一个计算机下国际象棋的功能，拥有完全的代码，由matlab编成的很多人工智能的函数，有详细的文档，直接运行chess.m文件即可-The program provides a computer under the functions of chess, with complete code, matlab compiled from many artificial intelligence function, has a detailed document, a document can be directly run chess.m

this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.-this demo is to show you how to implement a ge neric SIR (a.k.a. particle, the bootstrap. Monte Carlo) filter to estimate the hidden stat es of a nonlinear. non-Gaussian state space model.