bp神经网络计算的源程序

这个程序是A*算法的简单实现，来解决八数码问题-It just simply applys the A* algorithm to solve the Eghit-puzzle problem

人工神经网络算法研究及应用 田景文.包括神经网络、遗传算法、小波网络、模拟退火算法和支持向量机，清晰易懂，非常值得一看。-Artificial Neural Network Algorithm Research and Application田景文. Including neural networks, genetic algorithms, wavelet networks, simulated annealing algorithm and support vector machine, clear and easy to understand, is a must see.

本代码用蚁群算法求解带时间窗的车辆路径问题-The code with ant colony algorithm with time window of vehicle routing problem

水平有限，仅供参考！望大家多提宝贵意见！-limited, and for reference purposes only! Hope you will provide more valuable opinions!

用于求解TSP问题的c++程序，已经试运行过-BasicAntColon AlgorithmC++forTSP

以某区水文数据为例，在matlab7中构建BP神经网络，进行仿真预测。-Hydrological data in a certain district, for example, in matlab7 Building BP neural networks, simulation prediction.

人工智能方面一个专家系统的简单例子-artificial intelligence expert system for a simple example

单神经元pid源码实现，对初学的朋友很有帮助-The program can realize single neural pid with MATLAB.The algorithm is simple and easy to understand.

关联规则中的频繁项集生成算法genmax，可在linux和windows下编译运行，可能不太容易看懂。-association rules of frequent item sets genmax generation algorithm, and the Linux compiler running under windows, it may not be easy to understand.

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