direct_clustering.java~46~

分别是模糊识别、模糊聚类、模糊控制的实例代码

package fuzzy_project;

class Direct_Clustering {
  private int row;
  private int col;
  private int count;
  private byte cut_Matrix[][];
  private int Clustering_result[][];
  private int length[];        //记录二维数组Clustering_result中每行非零元素的个数
  private byte sign[];        //标记二维数组Clustering_result中每一行
  private String result;

  public Direct_Clustering(byte arrary[][],int r,int c)
  {
    cut_Matrix=arrary;
    row=r;
    col=c;
    count=0;
    Clustering_result=new int[r][c];
    length=new int[c];
    sign=new byte[r];
    result="#";
  }

  public String exe()
  {
    for(int i=0;i<row;i++)           //遍历截矩阵，又由于截矩阵是对称矩阵，所以只遍历上三角矩阵元素
    {
      for (int j = i; j < col; j++)
      {
        if (cut_Matrix[i][j] == 1)
        {
          Clustering_result[i][count++]=j+1;
        }
      }
      count=0;
    }

    for(int i=0;i<row;i++)           //记录二维数组Clustering_result中每行非零元素的个数
    {
      for (int j=0;j<col;j++)
      {
         if (Clustering_result[i][j]!=0)
          {
            count++;
          }
      }
      length[i]=count;           //length[i]的值代表二维数组Clustering_result中第i行的非零元素个数
      count=0;
    }


    for(int x=0;x<row;x++)         //生成邻接矩阵
      for(int y=1;y<length[x];y++)
      {
        if(Clustering_result[x][y]>Clustering_result[x][0])
        {
          Clustering_result[Clustering_result[x][y]-1][length[Clustering_result[x][y]-1]]=Clustering_result[x][0];
          length[Clustering_result[x][y]-1]++;
        }
      }
///////////////////////



//////////////////////打印Clustering_result矩阵
    for(int i=0;i<row;i++)
      {for(int j=0;j<col;j++)
        {
          System.out.print(Clustering_result[i][j]);
        }
        System.out.print("\n");
      }


//////////////////






    return result;
  }
}