一个完整BP神经网络源代码，包括数据和说明文档

BP神经网络自适应步长训练算法，采用最小误差法，梯度下降法，自适应调节权值-BP neural network training anaysis, is realized by using error feed back, gradient descent applied updating of synaptic weights

petri网络的演示程序，本程序是计算机网络课程中涉及到的petri网络的演示，输入预先的转换规则，搜索可以击发的状态，设定次数进行状态转换-Petri Network Demonstration Program, the process is computer network courses related to the petri network demonstration, the importation of pre-conversion rules can search the state while working on the creation of a state for the number change

支持向量机(SVM)的SMO算法实现。包括matlab源代码文件、支持向量机的pdf文档，源代码word文档。-Support Vector Machine (SVM) algorithm of SMO. Including the matlab source code files, Support Vector Machines pdf documents, source code for word documents.

神 经 网 络 的 资 料-neural network data

本算法为自适应遗传算法，采用基于排序的选择策略，编码方案为实数编码。能够解决很多优化问题！-The algorithm for the adaptive genetic algorithm, based on the sort of choice strategy, coding schemes for real-coded. Many optimization problems can be solved!

用模拟退火法高效的计算图论中哈密顿贿赂的最短路径-simulated annealing method of calculating efficient graph theory Hamiltonian bribery Shortest Path

神经网络算法-Neural network algorithm

用于求解多目标优化问题，多目标进化算法源程序-The code for multi-objective optimization algorithm

用C#语言开发基于模拟退火算法解决的10个城市的TSP问题-The Solution of TSP(C#)

MCRGSA------组播路由问题遗传模拟退火算法 ％M-----------遗传算法进化代数 ％N-----------种群规模，取偶数 ％Pm----------变异概率调节参数 ％K-----------同一温度下状态跳转次数 ％t0----------初始温度 ％alpha-------降温系数 ％beta--------浓度均衡系数 ％ROUTES------备选路径集 ％Num---------到各节点的备选路径数目 ％Cost--------费用邻接矩阵 ％Source------源节点标号 ％End---------目的节点标号组成的向量 ％MBR---------各代最优路径编码-MCRGSA------ Multicast Routing genetic simulated annealing% M----------- genetic algorithm algebra% N----------- population size, even take Pm----------% probability parameter variation%----------- same temperature K Jump under a number of state----------% t0 initial temperature cooling% alpha------- beta coefficient%-------- balanced concentration coefficient% ROUTES------ Alternative Path Set% Enable--------- nodes to the number of alternative paths%-------- Cost adjacent costs Matrix% Source------ source node labeling% End--------- destination node labeling Group Vector%% of the MBR--------- generations optimal path coding