HMM--Sim-vc6.zip_hmm sim线路库_hmmsim2 4

import java.util.Random; 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 < layernum.length; l++) { layer[l] = new double[layernum[l]]; layerErr[l] = new double[layernum[l]]; if (l + 1 < layernum.length) { layer_weight[l] = new double[layernum[l] + 1][layernum[l + 1]]; layer_weight_delta[l] = new double[layernum[l] + 1][layernum[l + 1]]; for (int j = 0; j < layernum[l] + 1; j++) { for (int i = 0; i < layernum[l + 1]; i++) { layer_weight[l][j][i] = random.nextDouble(); //随机初始化权重 } } } } } //逐层向前计算输出 public double[] computeOut (double[] in) { for (int l = 1; l < layer.length; l++) { for (int j = 0; j < layer[l].length; j++) { double z = layer_weight[l - 1][layer[l - 1].length][j]; for (int i = 0; i < layer[l - 1].length; i++) { layer[l - 1][i] = l == 1 ? in[i] : layer[l - 1][i]; z += layer_weight[l - 1][i][j] * layer[l - 1][i]; } layer[l][j] = 1 / (1 + Math.exp (-z) ); } } return layer[layer.length - 1]; } //逐层反向计算误差并修改权重 public void updateWeight (double[] tar) { int l = layer.length - 1; for (int j = 0; j < layerErr[l].length; j++) { layerErr[l][j] = layer[l][j] * (1 - layer[l][j]) * (tar[j] - layer[l][j]); } while (l-- > 0) { for (int j = 0; j < layerErr[l].length; j++) { double z = 0.0; for (int i = 0; i < layerErr[l + 1].length; i++) { z = z + l > 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); } }