ICA经典文章,入门必看。受益，高阶矩统计牲在信号处理中的应用，详细介绍许多重要概念。...

PID algorithm simulation program, including rk 四阶算法。-PID algorithm simulation program, including four bands rk algorithm.

人工智能学习的重要内容，对加强和巩固所学内容将起到非常重要的作用。-AI an important part of learning, to strengthen and consolidate the contents will play a very important role.

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

使用神经网络的K算法来对数据进行分类，有具体的实验要求和图-K using the neural network algorithm to classify the data, there is a specific test requirements and plans

kmeans算法的java描述，我自己编写的，可以运行的-java kmeans algorithm description, I am prepared, ready-to-run

k-means(欧氏距离)聚类算法是最基本的聚类算法，是理解和应用聚类算法的基础，通过k-means(欧氏距离)聚类算法我们才可以初步了解数据挖掘的原理。-k-means (Euclidean distance) clustering algorithm is the most basic clustering algorithm, is understanding and the basis for the application of clustering algorithm, through the k-means (Euclidean distance) clustering algorithm we are able to a preliminary understanding of the principles of data mining .

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

matlab神经网络应用设计-Matlab neural network application design

关于鱼群算法的一个程序，希望对学习智能算法的朋友有所帮助。-Fish-swarm algorithm on a procedure, and they hope to learn from intelligent algorithm friend help.

我们解决八数码问题,广度优先搜索可能会导致内存不够用,采用启发式搜索,启发函数为f(x)=g(x)+h(x) g(x)为该结点不同于目标结点的个数,h(x)为该结点的深度,选择那f(x)结点最小的那个结点进行扩展, 引入了一个"扩展数组[4]"(因为扩展的结点最多只有4个),该数组保存着某个结点的0点向各个方向的扩展的结点对象,然后对该扩展数组进行分析,利用启发函数在进行递归扩展... -us solve eight digital, BFS may lead to insufficient memory, using heuristic search, heuristic function f (x) = g (x) h (x) g (x) for the target node is different from the number of nodes, h (x) for the nodes of depth, the choice between f (x) nodes that the smallest node expansion. the introduction of an "expanded array [4]" (because the expansion of the nodes at most four), The array maintains a node of 0:00 a.m. to expand in all directions in the target node, and then expanded array analysis, use heuristic function during recursive expansion ...