PSO (粒子群优法)简单的范例程式 可以提供初学者使用

用元胞自动机实现以不同颜色表示不同状态细胞的细菌分裂试验的算法C语言代码-Using Cellular Automata in order to realize different colors express different states of bacterial cells to split the test algorithm C language code

MATLAB编写的BP神经网络程序，好用。-MATLAB prepared by BP neural network procedures, easy to use.

一种新的ica算法，主要是应用与盲分离算法，大家可以-ica a new algorithm, and are primarily used to blind separation algorithm, we can s

svm Main_SVR_Epsilon 用于svm回归的 在matlab中运用 -svm regression svm Main_SVR_Epsilon for use in matlab

介绍了神经 网络中的HOPFIELD算法的C语言实现.-Introduce a neural network algorithm HOPFIELD realize the C language.

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and ot

针对TSP问题，使用蚂蚁演算法求最佳解！程式还满美观唷！-against TSP, the use of the ant algorithm for the optimal solution! Program was one of appearance Police!

this about the artificial neural networks which uses the back propogation method.-this is about the artificial neural networks which uses the back propogation method.

介绍了神经 网络中的BPN算法的C语言实现.-Introduce a neural network BPN algorithm 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