累计BP与标准BP

src/main/java/org/wlld/App.java

@@ -19,7 +19,7 @@ public class App {
         //构建一个神经网络管理器，参数：(感知神经元个数，隐层神经元个数，输出神经元个数，输出神经元深度)
         //一个神经网络管理管理一个神经网络学习内容，
         NerveManager nerveManager =
-                new NerveManager(2, 4, 1, 4);
+                new NerveManager(2, 2, 1, 2);
         //开始构建神经网络，参数为是否初始化权重及阈值,若
         nerveManager.setStudyPoint(0.1);//设置学习率(取值范围是0-1开区间)，若不设置默认为0.1
         nerveManager.init(true);
@@ -33,10 +33,10 @@ public class App {
         List<List<Double>> testList = new ArrayList<>();
         for (int i = 0; i < 1000; i++) {
             List<Double> dm = new ArrayList<>();
-            dm.add(1.0);
-            dm.add(0.8);
-            dm.add(0.03);
-            dm.add(0.06);
+            dm.add(ArithUtil.add(1.0, random.nextDouble()));
+            dm.add(ArithUtil.add(0.8, random.nextDouble()));
+            dm.add(ArithUtil.add(-1.0, -random.nextDouble()));
+            dm.add(ArithUtil.add(-0.8, -random.nextDouble()));
             testList.add(dm);
         }
         for (int i = 0; i < 1000; i++) {
@@ -52,9 +52,9 @@ public class App {
         double hiddenTh = hiddenNerve.getThreshold();//隐层阈值
         double outTh = outNerver.getThreshold();//输出阈值
         System.out.println("hiddenTh==" + hiddenTh + ",outTh==" + outTh);
-        sensoryNerves.get(0).postMessage(1, 1.0, false, E1);
-        sensoryNerves.get(1).postMessage(1, 0.8, false, E1);
-        sensoryNerves.get(0).postMessage(1, 0.03, false, E2);
-        sensoryNerves.get(1).postMessage(1, 0.06, false, E2);
+        sensoryNerves.get(0).postMessage(1, 1.5, false, E1);
+        sensoryNerves.get(1).postMessage(1, 1.2, false, E1);
+        sensoryNerves.get(0).postMessage(1, -1.5, false, E2);
+        sensoryNerves.get(1).postMessage(1, -1.2, false, E2);
     }
 }

src/main/java/org/wlld/nerveEntity/OutNerve.java

@@ -55,8 +55,8 @@ public class OutNerve extends Nerve {
         //上层神经元输入值 * 当前神经元梯度*学习率 =该上层输入的神经元权重变化
         //当前梯度神经元梯度变化 *学习旅 * -1 = 当前神经元阈值变化
         //ArithUtil.sub(E, outNub) 求这个的累计平均值
-        allE = ArithUtil.add(Math.abs(ArithUtil.sub(E, outNub)), allE);
-        double avg = ArithUtil.div(allE, trainNub);
-        return ArithUtil.mul(activeFunction.sigmoidG(outNub), avg);
+        //allE = ArithUtil.add(Math.abs(ArithUtil.sub(E, outNub)), allE);
+        // double avg = ArithUtil.div(allE, trainNub);
+        return ArithUtil.mul(activeFunction.sigmoidG(outNub), ArithUtil.sub(E, outNub));
     }
 }