本程序实现通过构造一棵前缀树实现 最大模式频繁项集挖掘算法.应用fp树增长算法实现....

基于MATLAB完成的神经网络源程序 大家-Based on the MATLAB neural network to complete the U.S. source

计算机故障智能诊断系统 用户输入计算机的故障症状 即可由系统得出故障原因和相关的处理方法-Diagnose the system in computer s trouble intelligence Users input the trouble symptom of the computer Can be had cranky reason and relevant treatment methods systematically

用parzen窗方法，估计概率密度，采用高期核函数-With parzen window means of estimating the probability density function using high nucleus. . . .

采用C编写的BP神经网络一个程序，请提意见-with C BP prepared a neural network procedures, please comment

详细讲解了动态规划的算法问题，再通过“装箱问题”等7个程序实例介绍解决动态规划问题的思路和方法。-Gave a detailed algorithm for dynamic programming problems, and through the " bin-packing problem" and other instances of seven procedures introduced to solve the problem of dynamic programming ideas and methods.

TSP的智能算法，里面包含最大最小蚁群算法，蚂蚁系统，蚁群系统

模拟退火和遗传算法混和求解车辆路径问题的程序，没有解压密码。-simulated annealing and genetic algorithms hybrid vehicle routing problem solving process, not extracting passwords.

在vc控制台中运行，用遗传算法求解最短路径，用邻接矩阵表示路线图。-in vc console operations, Genetic Algorithm for the shortest path, adjacency matrices road map.

支持向量机实现程序，用matlab实现，那来和大家分享。-Support Vector Machines realize procedure, using matlab realize, that to share with you all.

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