clustherm.m

上传RBF源程序

clear,clc;
SamNum = 100;                       % 训练样本数
TestSamNum = 101;                   % 测试样本数
InDim = 1;                          % 样本输入维数
ClusterNum = 10;                    % 隐节点数，即聚类样本数
Overlap = 1.0;                      % 隐节点重叠系数

% 根据目标函数获得样本输入输出
rand('state',sum(100*clock))
NoiseVar = 0.1;
Noise = NoiseVar*randn(1,SamNum);
SamIn = 8*rand(1,SamNum)-4;
SamOutNoNoise = 1.1*(1-SamIn+2*SamIn.^2).*exp(-SamIn.^2/2);
SamOut = SamOutNoNoise + Noise;

TestSamIn = -4:0.08:4;
TestSamOut = 1.1*(1-TestSamIn+2*TestSamIn.^2).*exp(-TestSamIn.^2/2);

figure
hold on
grid
plot(SamIn,SamOut,'b+')
plot(TestSamIn,TestSamOut,'r--')
xlabel('Input x');
ylabel('Output y');

Centers = SamIn(:,1:ClusterNum);     %Use the first ten input as cluster centers

NumberInClusters = zeros(ClusterNum,1);             % 各类中的样本数，初始化为零
IndexInClusters = zeros(ClusterNum,SamNum);         % 各类所含样本的索引号
while 1,
    NumberInClusters = zeros(ClusterNum,1);         % 各类中的样本数，初始化为零
    IndexInClusters = zeros(ClusterNum,SamNum);     % 各类所含样本的索引号
    
    % 按最小距离原则对所有样本进行分类
    for i = 1:SamNum
        AllDistance = dist(Centers',SamIn(:,i));
        [MinDist,Pos] = min(AllDistance);
        NumberInClusters(Pos) = NumberInClusters(Pos) + 1;  %add one to the corresponding cluster centre
        IndexInClusters(Pos,NumberInClusters(Pos)) = i;     % add index to the cluster
    end
    
    % 保存旧的聚类中心
    OldCenters = Centers;   
    
    % 重新计算各类的聚类中心
    for i = 1:ClusterNum
        Index = IndexInClusters(i,1:NumberInClusters(i));
        Centers(:,i) = mean(SamIn(:,Index)')';                   %  STEP1 use average of the cluster as the centre
    end
 
    % 判断新旧聚类中心是否一致，是则结束聚类
    EqualNum = sum(sum(Centers==OldCenters));
    if EqualNum == InDim*ClusterNum,
        break,
    end
end

% 计算各隐节点的扩展常数（宽度）
AllDistances = dist(Centers',Centers);      % 计算隐节点数据中心间的距离（矩阵）
Maximum = max(max(AllDistances));           % 找出其中最大的一个距离
for i = 1:ClusterNum                        % 将距离矩阵的对角线上的0替换为较大的值，以免影响计算
    AllDistances(i,i) = Maximum+1;
end
Spreads = Overlap*min(AllDistances)';                           % STEP2 以隐节点间的最小距离作为扩展常数

% 计算各隐节点的输出权值
Distance = dist(Centers',SamIn);            % 计算各样本输入离各数据中心的距离
SpreadsMat = repmat(Spreads,1,SamNum);
HiddenUnitOut = radbas(Distance./SpreadsMat);           % 计算隐节点输出阵
HiddenUnitOutEx = [HiddenUnitOut' ones(SamNum,1)]';     % 将偏移纳入输出权值，故隐节点输出阵加一维
W2Ex = SamOut*pinv(HiddenUnitOutEx);                            % STEP3 用广义逆求广义输出权值
W2 = W2Ex(:,1:ClusterNum);                              % 输出权值
B2 = W2Ex(:,ClusterNum+1);                              % 偏移

% 测试
TestDistance = dist(Centers',TestSamIn);
TestSpreadsMat = repmat(Spreads,1,TestSamNum);
TestHiddenUnitOut = radbas(TestDistance./TestSpreadsMat);
TestNNOut = W2*TestHiddenUnitOut+B2;
plot(TestSamIn,TestNNOut,'k-');legend('Sample data','Original','RBF NN Curve');

W2
B2