diff --git a/lib/nn.js b/lib/nn.js
index 5cce746..7c20490 100644
--- a/lib/nn.js
+++ b/lib/nn.js
@@ -1,7 +1,7 @@
 // Other techniques for learning
 
-class ActivationFunction{
-  constructor(func, dfunc){
+class ActivationFunction {
+  constructor(func, dfunc) {
     this.func = func;
     this.dfunc = dfunc;
   }
@@ -9,12 +9,12 @@ class ActivationFunction{
 
 let sigmoid = new ActivationFunction(
   x => 1 / (1 + Math.exp(-x)),
-  y => y * (1- y)
+  y => y * (1 - y)
 );
 
 let tanh = new ActivationFunction(
   x => Math.tanh(x),
-  y => 1-(y*y)
+  y => 1 - (y * y)
 );
 
 
@@ -33,10 +33,15 @@ class NeuralNetwork {
     this.bias_o = new Matrix(this.output_nodes, 1);
     this.bias_h.randomize();
     this.bias_o.randomize();
-    this.setLearningRate();
 
-    this.setActivationFunction();
+    this.learningRateDecay = false;
+    this.decayRatio = 0;
+    this.numberOfTrainings = 0;
+    this.countOfTrainings = 0;
+
 
+    this.setLearningRate();
+    this.setActivationFunction();
   }
 
   predict(input_array) {
@@ -57,12 +62,35 @@ class NeuralNetwork {
     return output.toArray();
   }
 
+  setActivationFunction(func = sigmoid) {
+    this.activation_function = func;
+  }
+
   setLearningRate(learning_rate = 0.1) {
     this.learning_rate = learning_rate;
   }
 
-  setActivationFunction(func = sigmoid) {
-    this.activation_function = func;
+  enableLearningRateDecay(number_of_trainings = 1000, decay_ratio = 0.01) {
+    this.learningRateDecay = true;
+    this.numberOfTrainings = number_of_trainings;
+    this.decayRatio = decay_ratio;
+  }
+
+  disableLearningRateDecay() {
+    this.learningRateDecay = false;
+  }
+
+  checkLearningRateDecay() {
+    if (this.countOfTrainings > 0 && this.numberOfTrainings > 0 && this.learningRateDecay && this.learning_rate > 0) {
+      if (this.countOfTrainings % this.numberOfTrainings === 0) {
+        let newLearningRate = this.learning_rate * (1 - this.decayRatio);
+        if (newLearningRate <= 0) {
+          disableLearningRateDecay();
+          return;
+        }
+        this.setLearningRate(newLearningRate);
+      }
+    }
   }
 
   train(input_array, target_array) {
@@ -118,6 +146,9 @@ class NeuralNetwork {
     // Adjust the bias by its deltas (which is just the gradients)
     this.bias_h.add(hidden_gradient);
 
+    this.countOfTrainings++;
+    this.checkLearningRateDecay();
+
     // outputs.print();
     // targets.print();
     // error.print();
@@ -128,8 +159,7 @@ class NeuralNetwork {
   }
 
   static deserialize(data) {
-    if(typeof data == 'string')
-    {
+    if (typeof data == 'string') {
       data = JSON.parse(data);
     }
     let nn = new NeuralNetwork(data.input_nodes, data.hidden_nodes, data.output_nodes);