【SVM预测】基于风驱动算法改进SVM算法实现数据分类matlab源码-优快云博客

一、神经网络-支持向量机

支持向量机(Support Vector Machine)是Cortes和Vapnik于1995年首先提出的，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。 1 数学部分 1.1 二维空间【SVM预测】基于风驱动算法改进SVM算法实现数据分类matlab源码_预测模型 2 算法部分

二、风驱动算法

【SVM预测】基于风驱动算法改进SVM算法实现数据分类matlab源码_matlab_26

【SVM预测】基于风驱动算法改进SVM算法实现数据分类matlab源码_matlab_27

WDO算法的实现流程：
1) 初始化空气质点的个数和维度，定义最大迭代次数和相关的参数常量，设置搜索边界(位置和速度)，设置相应的测试函数。
2) 随机初始化各个质点的初始信息(位置和速度)，计算初始的压力值并根据压力值的大小升序排列。
3) 开始迭代。更新空气质点的速度和位置，计算质点压力值并以升序方式重新排列种群顺序。
4) 迭代终止。判断是否满足终止条件，如果不满足就返回步骤3)，否则就终止迭代，最后搜索到的最优位置就是最优解。

三、代码

tic;  
clear;  
close all;  
clc;  
format long g;
delete('WDOoutput.txt');  
delete('WDOpressure.txt');  
delete('WDOposition.txt');
fid=fopen('WDOoutput.txt','a');
%--------------------------------------------------------------

% User defined WDO parameters:
param.popsize = 20;		% population size.
param.npar = 5;			% Dimension of the problem.
param.maxit = 500;		% Maximum number of iterations.
param.RT = 3;			% RT coefficient.
param.g = 0.2;			% gravitational constant.
param.alp = 0.4;		% constants in the update eq.
param.c = 0.4;			% coriolis effect.
maxV = 0.3;			% maximum allowed speed.
dimMin =  -5;			% Lower dimension boundary.
dimMax= 5;			% Upper dimension boundary.
%---------------------------------------------------------------

% Initialize WDO population, position and velocity:
% Randomize population in the range of [-1, 1]:
pos = 2*(rand(param.popsize,param.npar)-0.5);
% Randomize velocity:
vel = maxV * 2 * (rand(param.popsize,param.npar)-0.5);  
	
%---------------------------------------------------------------
% Evaluate initial population: (Sphere Function)
for K=1:param.popsize,
	x = (dimMax - dimMin) * ((pos(K,:)+1)./2) + dimMin;
    	pres(K,:) = sum (x.^2);
end

%----------------------------------------------------------------
% Finding best air parcel in the initial population :
[globalpres,indx] = min(pres);
globalpos = pos(indx,:);
minpres(1) = min(pres);			% minimum pressure

%-----------------------------------------------------------------
% Rank the air parcels:
[sorted_pres rank_ind] = sort(pres);
% Sort the air parcels:
pos = pos(rank_ind,:);
keepglob(1) = globalpres;
%-----------------------------------------------------------------

% Start iterations :
iter = 1;   % iteration counter
for ij = 2:param.maxit,
    	% Update the velocity:
    	for i=1:param.popsize
		% choose random dimensions:
		a = randperm(param.npar);        			
		% choose velocity based on random dimension:
    		velot(i,:) = vel(i,a);				
        	vel(i,:) = (1-param.alp)*vel(i,:)-(param.g*pos(i,:))+ ...
				    abs(1-1/i)*((globalpos-pos(i,:)).*param.RT)+ ...
				    (param.c*velot(i,:)/i);
    	end
    
        	% Check velocity:
        	vel = min(vel, maxV);
        	vel = max(vel, -maxV);
		% Update air parcel positions:
    		pos = pos + vel;
        	pos = min(pos, 1.0);
        	pos = max(pos, -1.0); 
		% Evaluate population: (Pressure)
		for K=1:param.popsize,
			x = (dimMax - dimMin) * ((pos(K,:)+1)./2) + dimMin;
    			pres(K,:) = sum (x.^2);
		end

    	%----------------------------------------------------
    	% Finding best particle in population
    	[minpres,indx] = min(pres);
    	minpos = pos(indx,:);           	% min location for this iteration
    	%----------------------------------------------------
    	% Rank the air parcels:
    	[sorted_pres rank_ind] = sort(pres);
    	% Sort the air parcels position, velocity and pressure:
    	pos = pos(rank_ind,:);
    	vel = vel(rank_ind,:);
    	pres = sorted_pres;  
    
    	% Updating the global best:
    	better = minpres < globalpres;
    	if better
        		globalpres = minpres             % initialize global minimum
        		globalpos = minpos;
   	end
	% Keep a record of the progress:
    	keepglob(ij) = globalpres;
    	save WDOposition.txt pos -ascii -tabs;
end
	%Save values to the final file.
    	pressure = transpose(keepglob);
    	save WDOpressure.txt pressure -ascii -tabs;
    	%END
%-----------------------------------------------------