c语言版基本粒子群算法源程序

分类算法是数据挖掘中最为重要的技术之通过介绍当前最具有代表性的分类算法,分析并总结了各种算法 -NULL

可以自动识别一个程序中调用的所有函数，并写入到 output.txt文件中。-can automatically identify a process called all functions, and write to output.txt document.

neural network in Java

一个关于神经网络遗传算法的源程序，对于初学者肯定有帮助-A source code about NN(nerve network),it is helpful for beginners

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

蚁群算法源代码，一个老外的例子-ant colony algorithm source code and an example of a foreigner

模拟退火c++的算法程序，广泛应用于最优化、运筹学、人工智能、遗传算法等领域，具有很好的学习价值-simulated annealing algorithm procedures, the most widely used optimization, operations research, artificial intelligence, genetic algorithms and other areas of learning is very good value

遗传算法的文章- Heredity algorithm article

销售预测系统，可以根据历史销售数据对未来的销售量进行有效预测，采用BP神经网络对预测模型进行训练，可以达到不错效果-sales forecasting system, based on historical sales data for the future effective sales forecasts, BP neural network model to predict the training, they can achieve good results

程序演示的是用简单遗传算法随机一个种群，然后根据所给的交叉率，变异率，世代数计算最大适应度所在的代数-Program demonstrates that the simple genetic algorithm with a random population, then according to the crossover rate, mutation rate, calculating the maximum number of generations where the fitness of algebraic