diff --git a/src/main/java/org/dromara/easyai/transFormer/nerve/HiddenNerve.java b/src/main/java/org/dromara/easyai/transFormer/nerve/HiddenNerve.java
new file mode 100644
index 0000000..0ac02ae
--- /dev/null
+++ b/src/main/java/org/dromara/easyai/transFormer/nerve/HiddenNerve.java
@@ -0,0 +1,51 @@
+package org.dromara.easyai.transFormer.nerve;
+
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.i.ActiveFunction;
+import org.dromara.easyai.i.OutBack;
+import org.dromara.easyai.transFormer.LineBlock;
+
+import java.util.List;
+
+/**
+ * @author lidapeng
+ * 隐层神经元
+ * @date  9:30 上午 2019/12/21
+ */
+public class HiddenNerve extends Nerve {
+
+    public HiddenNerve(int id, int depth, float studyPoint, ActiveFunction activeFunction, int sensoryNerveNub,
+                       int outNerveNub, LineBlock lineBlock, int regularModel, float regular
+            , int coreNumber) throws Exception {//隐层神经元
+        super(id, "HiddenNerve", studyPoint, activeFunction, sensoryNerveNub, 0,
+                outNerveNub, lineBlock, regularModel, regular, coreNumber);
+        this.depth = depth;
+    }
+
+    public void receiveErrorMatrix(Matrix g, long eventId, Matrix allError) throws Exception {//
+        updatePower(eventId, g, allError);
+    }
+
+    public void receive(Matrix feature, long eventId, boolean isStudy, OutBack outBack,
+                        List<Integer> E, Matrix encoderFeature, boolean outAllPro) throws Exception {//接收上一个残差层传过来得参数
+        Matrix out = opMatrix(feature, isStudy);
+        sendMessage(eventId, out, isStudy, feature, outBack, E, encoderFeature, outAllPro);
+    }
+
+    @Override
+    protected void input(long eventId, Matrix parameter, boolean isStudy, Matrix allFeature, OutBack outBack,
+                         List<Integer> E, Matrix encoderFeature, boolean outAllPro) throws Exception {//第二层收到参数
+        boolean allReady = insertMatrixParameter(eventId, parameter);
+        if (allReady) {//参数齐了，开始计算
+            Matrix out = opMatrix(reMatrixFeatures.get(eventId).getMatrix(), isStudy);
+            reMatrixFeatures.remove(eventId);
+            beforeLayNorm.addNormFromNerve(eventId, isStudy, out, allFeature, outBack, E, encoderFeature, outAllPro);
+        }
+    }
+
+    public void postMessage(long eventId, Matrix feature, boolean isStudy, OutBack outBack, List<Integer> E, boolean outAllPro) throws Exception {
+        Matrix out = opMatrix(feature, isStudy);
+        sendOutMessage(eventId, out, isStudy, outBack, E, outAllPro);
+    }
+}