rbf_Kmeans.rar_K._RBF clustering_RBF k均值聚类_kmeans+RBF_kmeans聚类方法

%一个最基本的rbf算法，学习算法采用伪逆函数 % % % % % % % % % % % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear all; close all; clc; %定义用到的变量 r=1; %领域半径，人为设定 nodenum=0; %隐层节点数，由实际数据与r共同决定 inputdata=[]; %输入矩阵 inputpath=' '; %存储原始输入数据的路径 nodeout=[]; %隐层输入阵　 netout=[]; %网络输出阵 weight=[]; %输出权值阵，也是唯一的权值 inputnum=0; %输入维数 outputnum=0; %输出维数 center=[]; %聚类中心 numtrain=0; %学习样本的个数 row=0; %学习样本的个数 simrow=0; %全部样本的个数 numtest=0; %泛化样本的个数 strength=1; %归一化处理时用到的范围，一般为1 yout=[]; %输出的期望值 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %变量的初始化 r=1; inputnum=9; inputpath='e:\yinjia\data\yearsun.dat'; outputnum=1; simrow=290; source=load(inputpath); %保存一个source的副本，为反归一化做准备 copysource=source; %归一化处理 source=normalize(source,strength); yout=source(inputnum+1:simrow+inputnum); inputdata=phasespace(source,inputnum,simrow); row=250; numtrain=row; numtest=simrow-row; %把第一人输入变量定为初始的中心 %中心的位置在初始化后，不会再变化 center=inputdata(1,:)'; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %开始进行简单的聚类 %其基本思想是：给定一个固定的邻域半径，在中心点邻域半径内的点就算作 %是该邻域的点；半径外的点，取第一个不是半径内的点做为新的中心点 %设计一个函数iscenter,来计算一个点是否为中心点。 for step=2:row if iscenter(inputdata(step,:)',center,r) center=[center inputdata(step,:)']; nodenum=nodenum+1; end % this end for iscenter(inputdata(step,:)) end % this end for step=2:row %聚类完成. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %准备进行神经网络的前向计算 [centerrow nodenum]=size(center); %网络的初始化 nodeout=zeros(row,nodenum); netout=zeros(row,outputnum); weight=zeros(nodenum,outputnum); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %网络的计算 for step=1:row for step1=1:nodenum nodeout(step,step1)=GaussRadialBasisFunction(inputdata(step,:)',... center(:,step1),r); end % this end for step1=1:nodenum end %this end for step=1:row %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %用伪逆来计算权值 weight=pinv(nodeout)*yout(1:250); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %进行仿真 for step=1:simrow for step1=1:nodenum nodeout(step,step1)=GaussRadialBasisFunction(inputdata(step,:)',... center(:,step1),r); end % this end for step1=1:nodenum end %this end for step=1:simrow mydata=nodeout*weight; %反归一化 mydata=unnomal(mydata,copysource,strength); rldata=copysource(inputnum+1:inputnum+simrow); plot(rldata);hold on;plot(mydata,'r'); %计算一个评价函数 rmsetest=(norm(rldata(numtrain+1:simrow)-mydata(numtrain+1:simrow))... ^2/(numtest-1))^0.5