Prolog开发的简单的几个人工智能方面的程序.含问题分析报告.

为模型参数中的动静比例知道的情况 属于一种新的模型 - The situation knew which for the model parameter in sound of something astir proportion belongs to one kind of new model

经典的数据关联挖掘算法prefixspan-Classical data association mining algorithm PrefixSpan

基于神经网络的汉语语音情感识别的研究。很好的描述了人工神经网络的方面-Neural Network Based Speech Emotion Recognition in Chinese studies. Good description of the artificial neural network connection

它是一个基于遗传算法的优化方法，使用微分运算符。它用于解决带有大量未知事物的优化问题。-it is based on a genetic algorithm optimization method, the use of Differential Operators. It used to solve a large number of unknown things with the optimization problem.

基于主成份分析（PCA）的人脸特征识别核心源程序。

这个程序是个神经网络提供学习能力的实例，是前一个simpleann的功能加强版。-this procedure is a neural network learning ability example is the former simpleann an enhanced version of the function.

模糊ISODATA聚类分析，可以模糊矩阵数据进行聚类分析-Fuzzy ISODATA cluster analysis can be fuzzy matrix cluster analysis of data

数据挖掘中Aprior算法的C++实现，采用的传统Aprior算法-Aprior data mining algorithms to achieve C, the traditional algorithm Aprior

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

支持向量机(3)svm_light-Support Vector Machines(3):svm_light