1、算法实现

public class BpDeep{    public double[][] layer;//神经网络各层节点    public double[][] layerErr;//神经网络各节点误差    public double[][][] layer_weight;//各层节点权重    public double[][][] layer_weight_delta;//各层节点权重动量    public double mobp;//动量系数    public double rate;//学习系数    public BpDeep(int[] layernum, double rate, double mobp){        this.mobp = mobp;        this.rate = rate;        layer = new double[layernum.length][];        layerErr = new double[layernum.length][];        layer_weight = new double[layernum.length][][];        layer_weight_delta = new double[layernum.length][][];        Random random = new Random();        for(int l=0;l
    
     0){            for(int j=0;j
     
      0?layerErr[l+1][i]*layer_weight[l][j][i]:0;                    layer_weight_delta[l][j][i]= mobp*layer_weight_delta[l][j][i]+rate*layerErr[l+1][i]*layer[l][j];//隐含层动量调整                    layer_weight[l][j][i]+=layer_weight_delta[l][j][i];//隐含层权重调整                    if(j==layerErr[l].length-1){                        layer_weight_delta[l][j+1][i]= mobp*layer_weight_delta[l][j+1][i]+rate*layerErr[l+1][i];//截距动量调整                        layer_weight[l][j+1][i]+=layer_weight_delta[l][j+1][i];//截距权重调整                    }                }                layerErr[l][j]=z*layer[l][j]*(1-layer[l][j]);//记录误差            }        }    }    public void train(double[] in, double[] tar){        double[] out = computeOut(in);        updateWeight(tar);    }}
     
    

2、算法测试

public class BpDeepTest{    public static void main(String[] args){        //初始化神经网络的基本配置        //第一个参数是一个整型数组，表示神经网络的层数和每层节点数，比如{3,10,10,10,10,2}表示输入层是3个节点，输出层是2个节点，中间有4层隐含层，每层10个节点        //第二个参数是学习步长，第三个参数是动量系数        BpDeep bp = new BpDeep(new int[]{2,10,2}, 0.15, 0.8);        //设置样本数据，对应上面的4个二维坐标数据        double[][] data = new double[][]{
   {1,2},{2,2},{1,1},{2,1}};        //设置目标数据，对应4个坐标数据的分类        double[][] target = new double[][]{
   {1,0},{0,1},{0,1},{1,0}};        //迭代训练5000次        for(int n=0;n<5000;n++)            for(int i=0;i

3、执行结果

[1.0, 2.0]:[0.9782137336790337, 0.021683706747676907][2.0, 2.0]:[0.02140104439139772, 0.9785416755641893][1.0, 1.0]:[0.016850236680035113, 0.9835668738330479][2.0, 1.0]:[0.9809725214354169, 0.018824324694218176][3.0, 1.0]:[0.9985448434744455, 0.0013163425493131222]