Support Vector Machines realize procedure, using matlab realize, that to share w...

复数信号的FASTICA算法，和ICA算法类似，但主要用于处理复数信号。-Plural signals FastICA algorithm, and the ICA algorithm similar to, but mainly used for dealing with a plurality of signals.

有关优化的遗传算法-the genetic algorithm optimization

SPEA2源码，效果相当好 ZIZTLER系列复杂函数只有第三个效果不太好-SPEA2 source, the effect ZIZTLER very good series of complex functions, only the third effect is not a good

Genetic Algorithm and Its application is recommended System

模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S―R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S "S" = S+ sign (). delta where given for the step, [-1,1] Random Number

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

主要实现了烧结矿碱度的神经网络预测，训练精度高-The main achievement of the sinter basicity of the neural network prediction, training and high precision

利用a星算法来走迷宫的C++实例。console下运行，a星算法比较简洁明了，便于学习-The use of a satellite algorithm to Maze in C++ examples. console running, a star algorithm is relatively simple and clear, easy to learn

为模型参数中的动静比例知道的情况 属于一种新的模型 - The situation knew which for the model parameter in sound of something astir proportion belongs to one kind of new model

运用MATLAB语言编写的连续型霍普菲尔德网络-using MATLAB prepared by the continuous Hopfield network