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Description: 用matlab实现的贝叶斯网优化的fitness函数
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Size: 212 |
Author: jscarl |
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Description: 健身球检验分类机MATLAB应用--音乐信号处理MATLAB应用--音乐信号处理-fitness test signal separation machines MATLAB applications-- music MATLAB signal processing applications-- music Signal Processing
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Size: 254976 |
Author: 康抗 |
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Description: 提供一个人工免疫算法源程序，其算法过程包括：
1.设置各参数
2.随机产生初始群体——pop=initpop(popsize,chromlength)
3.故障类型编码，每一行为一种！code(1,:)，正常；code(2,:)，50％；code(3,:)，150％。实际故障测得数据编码，这里Unnoralcode,188％
4.开始迭代（M次）：
1）计算目标函数值：欧氏距离[objvalue]=calobjvalue(pop,i)
2）计算群体中每个个体的适应度fitvalue=calfitvalue(objvalue)
3）选择newpop=selection(pop,fitvalue) objvalue=calobjvalue(newpop,i) ％
交叉newpop=crossover(newpop,pc,k) objvalue=calobjvalue(newpop,i) ％
变异newpop=mutation(newpop,pm) objvalue=calobjvalue(newpop,i) ％
5.求出群体中适应值最大的个体及其适应值
6.迭代停止判断。-provide a source of artificial immune algorithm, the algorithm process include : 1. Two of the parameters set. Initial randomly generated groups-- pop = initpop (popsize, chromlength) 3. Fault type coding, each act a! Code (1 :), normal; Code (2, :), 50%; Code (3 :), 150%. Fault actual measured data coding, here Unnoralcode, 188% 4. Beginning iteration (M) : 1) the objective function value : Euclidean distance [objvalue] = calobjvalue (pop, i) 2) calculation of each individual groups of fitness calfitvalue fitvalue = ( objvalue) 3) = newpop choice selection (pop, fitvalue) objvalue = calobjvalue (newpop, i) =% newpop cross-crossover (newpop, pc, k) = calobjvalue objvalue (newpop, i) =% variation newpop mutation (newpop, pm ) objvalue = calobjvalue (newpop, i)% 5. groups sought to adapt th
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Size: 9216 |
Author: 江泉 |
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Description: pso 程序，一共三个，DeJong.m，get_psoOptions.m，pso.m结合起来就可以了，直接拷到work目录下就可以运行，其中get_psooptions中可以改设置，变成自己的；
DeJong是适应函数，也可以改。-PSO procedures, a total of three, DeJong.m, get_psoOptions.m, pso.m combine, and they can work directly Manchester directory on the run, which get_psooptions can change settings, into its own; DeJong is the fitness function, but also to change.
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Size: 1024 |
Author: jun |
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Description: A hybrid Particle Swarm Optimization algorithm for finding the minimum of
the function fitness in the real space.-Particle Swarm Optimization algo abbreviation for finding the minimum of the function fi tness in the real space.
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Size: 2048 |
Author: chen |
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Description: 遗传算法优化小波神经网络的源程序:
1.构造的非线性函数: 位于nninit_test.m
2.直接用WNN逼近非线性:Wnn_test.m, (内部调用小波函数)
3.遗传算法优化后逼近 :GA_Wnn_test.m (内部调用遗传算法的,初始化,适应度,解码函数)-genetic algorithm optimization WNN source : 1. Construction of the nonlinear function : nninit_test.m at 2. WNN directly with nonlinear approximation : Wnn_test.m. (internal called wavelet function) 3. Genetic Algorithm optimization approach : GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions)
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Size: 7168 |
Author: 李为 |
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Description: 免疫遗传算法matlab 程序,该算法由抗原识别、初始抗体产生、适应度计算、向记忆细胞分化、抗体的促进和抑制、抗体产生(交叉、变异) 六个模块组成-immune genetic algorithm Matlab procedure, the algorithm by antigen identification, initial antibody, fitness, to the memory cells, antibody for the promotion and inhibition, antibody (crossover and mutation) six modules
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Size: 1024 |
Author: He Hong |
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Description: 程序部分代码介绍：
evalops是传递给适应度函数的参数，opts是二进制编码的精度，termops是选择maxGenTerm结束函数时传递个maxGenTerm的参数，即遗传代数。xoverops是传递给交叉函数的参数。mutops是传递给变异函数的参数，具体含义我也没弄懂，我觉得有点怪。-Procedures introduce some code: evalops is passed to the fitness function parameters, opts is the precision of binary code, termops is to choose the end of maxGenTerm months maxGenTerm transfer function parameters, namely, genetic algebra. xoverops is passed to cross-function parameters. mutops is passed to the variogram parameters, the specific meaning he or she I do, I feel a bit strange.
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Size: 2048 |
Author: 钱广 |
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Description: 模拟一群鸟捕食的情景，从而达到优化目标函数的目的，这就是粒子群算法！起初在可行的空间中随机的产生一群粒子，然后让每个粒子开始在虚拟的空间中向四面八方飞翔，并且每个粒子都记下他们飞过的适应值（也就是目标优化函数）最高的点，而且整个粒子群有一个最高适应值个体，这样，粒子在飞翔的时候尽量朝向自己曾飞过的最好的点和集体的最好的点。最后达到收敛到近似最优点的目的。
-Simulation of a group of birds preying on the scene, so as to achieve the purpose of optimizing the objective function, that is, PSO! At first, where feasible, have a space in a group of random particles, and then let the beginning of each particle in a virtual space to fly in all directions, and each particle they have in mind over the fitness value (that is objective optimization function) the highest point , and the whole particle swarm adaptation has a maximum value of the individual, so that particles in the fly when he had flown as far as possible towards the best point and collective best point. Finally reaching the merits of convergence to approximate most purposes.
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Size: 4096 |
Author: chen |
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Description: 用遗传算法优化BP神经网络的Matlab编程适应值函数-Using genetic algorithms to optimize BP neural network fitness function Matlab programming
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Size: 1024 |
Author: fsdjoe |
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Description: 个程序就是最基本的粒子群优化算法程序，用Matlab实现，非常简单。是主函数的源程序，优化函数则以m文件的形式放在fitness.m里面，对不同的优化函数只要修改fitness.m就可以了通用性很强。-Procedures is the most basic particle swarm optimization procedures, using Matlab realize, is very simple. Is the main function of the source, optimizing the function with m the form of documents on fitness.m inside, optimized for different functions as long as the modifications can fitness.m highly generic.
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Size: 1024 |
Author: 牛牛 |
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Description: 基本粒子群优化算法Matlab源程序,其中的fitness函数可根据自己需要更改。-Elementary particle swarm optimization algorithm Matlab source code, in which the fitness function may need to change according to their own.
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Size: 4096 |
Author: 徐楠 |
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Description: 小波神经网络的源程序: 1.构造的非线性函数: 位于nninit_test.m 2.直接用WNN逼近非线性:Wnn_test.m, (内部调用小波函数) 3.遗传算法优化后逼近 :GA_Wnn_test.m (内部调用遗传算法的,初始化,适应度,解码函数)-genetic algorithm optimization WNN source : 1. Construction of the nonlinear function : nninit_test.m at 2. WNN directly with nonlinear approximation : Wnn_test.m. (internal called wavelet function) 3. Genetic Algorithm optimization approach : GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions) -Wavelet neural network source code: 1. Construction of the nonlinear function: at nninit_test.m 2. Wnn the direct use of nonlinear approximation: Wnn_test.m, (internal call wavelet function) 3. Genetic algorithm optimized approximation: GA_Wnn_test. m (internal call genetic algorithms, initialization, fitness, decoding function)-genetic algorithm optimization WNN source: 1. Construction of the nonlinear function: nninit_test.m at 2. WNN directly with nonlinear approximation: Wnn_test.m. ( internal called wavelet function) 3. Genetic Algorithm optimization approach: GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions)
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Size: 1024 |
Author: lanhucx |
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Description: Matlab中路径优化程序，销售员模式，最佳路径优化-Medium Matlab path optimization procedures, sales model, the best path optimization
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Size: 1024 |
Author: Robin |
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Description: 遗传算法整套源程序，包括初始种群，计算适应度值，选择，交叉变异等-Genetic algorithm source code package, including the initial population, calculation of fitness value, selection, crossover and other variant
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Size: 2048 |
Author: Mike |
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Description: Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.)
This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called lbest. when a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest. Following is the steps of PSO:
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Size: 1024 |
Author: BBB |
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Description: 遗传算法的基本步骤如下：
1）在一定编码方案下，随机产生一个初始种群；
2）用相应的解码方法，将编码后的个体转换成问
题空间的决策变量，并求得个体的适应值；
3）按照个体适应值的大小，从种群中选出适应值
较大的一些个体构成交配池；
4）由交叉和变异这两个遗传算子对交配池中的
个体进行操作，并形成新一代的种群；
5）反复执行步骤2-4，直至满足收敛判据为止。(The basic steps of the genetic algorithm are as follows:
1) under certain coding schemes, an initial population is randomly generated;
2) use the corresponding decoding method to convert the encoded individuals into questions
The decision variable of the problem space is obtained and the fitness value of the individual is obtained;
3) according to the size of individual fitness, the fitness is selected from the population
Larger individuals constitute mating pools;
4) by crossover and mutation, these two genetic operators are pairs of mating pools
Individuals operate and form a new generation of populations;
5) repeat step 2-4 until the convergence criterion is satisfied.)
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Size: 76800 |
Author: 傲视天下
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Description: % Bee Colony Optimization in Matlab
% By Adnan ACAN
% Each potential solution is called a Food Source
% Fitness is determined in terms of the quality of food source
% There are three groups of bees: employed bees, onlooker bees, and scout bees.
% Number of employed bees is equal to the number of onlooker bees.
% Employed bees search for the food sorces and gather information on the
% quality of foos sources. Onlooker bees stay in hive and search for food
% sources on the basis of information gathered by employed bees. The scout
% bees search for new food sources randomly.
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Size: 2048 |
Author: hichem22 |
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Description: 本代码可用来提取计算心率变异性非线性域特征值（SampEnVal, AlphaDFA, AlphaDFA1, AlphaDFA2 ）(Extract Nonlinear features of HRV.
%Inputs: RRI = inter-beat interval (s) and time locations (s)
% opt = analysis options from gui
%
%Outputs: output is a structure containg all HRV.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2010, John T. Ramshur, jramshur@gmail.com
%
% This file is part of HRVAS
%
% HRVAS is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% HRVAS is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.)
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Size: 305152 |
Author: HeJimin |
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Description: Dynamic linear calibration fitness function
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Size: 2070 |
Author: kimhanxin@163.com |
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