粒子群算法的示例程序，不错。可用来进行函数优化、神经网络训练等。...

基于粒子滤波原理，选取纹理特征对目标进行描述，实现视频目标的跟踪。-Principle-based particle filter, select the texture features to describe on the target to achieve the objectives of tracking video.

产生应用区系统中的噪声可以改变其分布的-Application areas have a system noise can change its distribution

人工智能中的全局搜索的九宫问题用VC实现 这下九宫问题的三种搜索方法都齐了-artificial intelligence of the global search JiuGongTu problems with VC JiuGongTu this issue under the three search methods are here

使用Astar算法实现的八数码问题。C++编写，控制台应用程序，实现简单，代码轻便。-Astar algorithm using eight digital issues. C++ written in the console application, to achieve simple and lightweight code.

一个改进的聚类算法FCMA,可以实现数据的自适应有效聚类,对2维数据测试效果较好- An improvement gathers kind of algorithm FCMA, may auto-adapted realize the data effectively to gather the kind, is better to 2 dimensions according to the test effect

基于Viusal C++开发的遗传算法源程序，在VC6.0环境下调试通过。-Based on Viusal C++ Developed genetic algorithm source code in VC6.0 environment debug through.

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

利用hopfield神经网络解决TSP(旅行商)问题-use hopfield neural network to solve TSP salesman problem (TSP)

采用动量梯度下降算法训练 BP 网络。 -Gradient descent algorithm using momentum BP network training.