这是一个将n个样本聚类到m个类别中 将n个样本在m个类里如何分配输出的遗传算法代码。 适应度：sum(i=1_110)sum(j=1_20)【xi...

人工智能搜索法解决九宫重排问题（又称8数码问题）。-artificial intelligence search JiuGongTu rearrangement solve the problem (also known as the Digital 8).

基于神经网络的文字识别系统,它通过遗传算法实现。-Neural Network Based Character Recognition system, which through genetic algorithm.

一个简单的fisher分类器代码,并含有实验用的两个数据集合.-a simple fisher classifier code, and contains the two experimental data sets.

详细介绍了SVM 和RVM的区别及各自的优点，说明RVM在很多方面优于SVM-Described in detail the difference between SVM and the RVM and their respective advantages, indicating RVM in many ways superior to SVM

SCS-C is another port to C of Goldberg s Simple Classifier System, with a few extensions.-SCS-C is another port to C of Goldberg s Simple Classifier System, with a few extensions.

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)

neural network in C language

仿真1：首先把网络温度参数T固定在100，按工作规则共进行1000次状态更新,把这1000次状态转移中网络中的各个状态出现的次数Si（i＝1，2，…，16）记录下来 按下式计算各个状态出现的实际频率： Pi=Si/∑i=1,NSi=Si/M 同时按照Bo1tzmann分布计算网络各个状态出现概率的理论值： Q（Ei）=（1/Z）exp（-Ei/T） 仿真2：实施降温方案，重新计算 采用快速降温方案：T（t）= T0/（1+t） T从1000降到0.01，按工作规则更新网络状态 当T=0.01时结束降温，再让T保持在0.01进行1000次状态转移，比较两种概率-a simulation : First of all network parameters temperature T fixed at 100 and, according to the rules for a total of 1000 to update the state, this state of the 1000 network transfer of all states for the number of Si (i = 1, 2, ..., 16) all recorded determined by the formula state-of the actual frequency : Pi = Si/i = 1, NSi = Si/M in accordance with Bo1tzmann distributed computing network of states all probability the theoretical value : Q (Ei) = (1/Z) exp (- Ei/T) Simulation 2 : implementation of cooling, re-using rapid cooling programs : T (t) = T0/(1 t) T dropped to 0.01 from 1000 and, according to the rules updated network state when T = 0.01 at the end of cooling, let T at 0.01 for the 1000 state tran

计算欧几里德距离C语言源代码,次源码经测试可有效运行,源码简单易懂-Calculation of Euclidean distance C-language source code, sub-source has been tested and can be effectively run, easy-to-read source

ID3算法有一定的自学习、自组织能力,适用于复杂系统的智能诊断。以实例验证了该算法对RH-KTB真空抽气系统故障诊断的有效性。-ID3 algorithm to a certain extent self-learning, self-organization, applied to a complex system of intelligent diagnosis. Examples to show that the algorithm of RH- KTB vacuum exhaust system fault diagnosis arrival.