转炉炼钢终点优化控制模型——matlab代码C、T预测

% https://blog.csdn.net/qq_25990967/article/details/122969769 % 功能：kmean聚类分析之野值剔除 % 作者：杨铮 % 单位：河钢数字信达 clear;clc; data = xlsread('data.xlsx',1,'B2:L90'); % 原理推导K均值 [m,n]=size(data); %读取数据的行数与列数 cluster_num=4; %自定义分类数 cluster=data(randperm(m,cluster_num),:); epoch_max=1000;%最大次数 therad_lim=0.001;%中心变化阈值 epoch_num=0; while(epoch_num<epoch_max) epoch_num=epoch_num+1; for i=1:cluster_num distance=(data-repmat(cluster(i,:),m,1)).^2; distance1(:,i)=sqrt(sum(distance')); end [~,index_cluster]=min(distance1'); for j=1:cluster_num cluster_new(j,:)=mean(data(find(index_cluster==j),:)); end if (sqrt(sum((cluster_new-cluster).^2))>therad_lim) cluster=cluster_new; else break; end end % 画出聚类效果 figure subplot(2,1,1) %画子图，在这里是一图上可画两张子图 a=unique(index_cluster); %找出分类出的个数 C=cell(1,length(a)); for i=1:length(a) C(1,i)={find(index_cluster==a(i))}; end for j=1:cluster_num data_get=data(C{1,j},:); scatter(data_get(:,1),data_get(:,2),100,'filled','MarkerFaceAlpha',.6,'MarkerEdgeAlpha',.9); hold on end plot(cluster(:,1),cluster(:,2),'kd','LineWidth',2); hold on sc_t=mean(silhouette(data,index_cluster')); title_str=['原理推导K均值聚类',' 聚类数为：',num2str(cluster_num),' SC轮廓系数:',num2str(sc_t)]; title(title_str) % MATLAB自带kmeans函数 subplot(2,1,2) %画子图，在这里是一图上可画两张子图 % cluster_num=4; %自定义分类数 [index_km,center_km]=kmeans(data,cluster_num) ;%MATLAB自带kmeans函数 a=unique(index_km); %找出分类出的个数 C=cell(1,length(a)); for i=1:length(a) C(1,i)={find(index_km==a(i))}; end for j=1:cluster_num data_get=data(C{1,j},:); scatter(data_get(:,1),data_get(:,2),100,'filled','MarkerFaceAlpha',.6,'MarkerEdgeAlpha',.9); hold on end plot(center_km(:,1),center_km(:,2),'kd','LineWidth',2); hold on sc_k=mean(silhouette(data,index_km)); title_str1=['MATLAB自带kmeans函数',' 聚类数为：',num2str(cluster_num),' SC轮廓系数:',num2str(sc_k)]; title(title_str1)