Update Example weights

Better parameters based on dynamic optimization. Co-authored-by: linuslangenkamp <[email protected]>
AMIT-HSBI · Jan 25, 2025 · 04d76e5 · 04d76e5
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diff --git a/NeuralNetwork/Examples/HelloWorld.mo b/NeuralNetwork/Examples/HelloWorld.mo
@@ -10,6 +10,7 @@ model HelloWorld
     Placement(transformation(origin = {40, -30}, extent = {{-20, -20}, {20, 20}})));
   Real reference = eq.y;
   Real prediction = nn.y;
+  Real error = reference - prediction;
 equation
   connect(sine.y, eq.u) annotation(
     Line(points = {{-38, 0}, {-20, 0}, {-20, 30}, {16, 30}}, color = {0, 0, 127}));

diff --git a/NeuralNetwork/Examples/Utilities/SimpleNetwork.mo b/NeuralNetwork/Examples/Utilities/SimpleNetwork.mo
@@ -15,10 +15,10 @@ block SimpleNetwork "Neural Network approximating y = u*u + 0.5*u - 2.0 on inter
   ) annotation(
     Placement(transformation(origin = {50, 0}, extent = {{-30, -30}, {30, 30}})));
 
-  parameter Real[2,1] layer_1_weights = {{-0.95248}, {-0.943175}};
-  parameter Real[2] layer_1_bias = {0.872633, -0.949252};
-  parameter Real[1,2] layer_2_weights = {{-2.25385, 1.40389}};
-  parameter Real[1] layer_2_bias = {0.60548};
+  parameter Real[2,1] layer_1_weights = {{-0.7039596170222842}, {-0.8750912730224503}};
+  parameter Real[2] layer_1_bias = {1.169784394444224, -1.344723440170712};
+  parameter Real[1,2] layer_2_weights = {{-4.513856227908402, 2.4932885356708683}};
+  parameter Real[1] layer_2_bias = {3.897109323824147};
 equation
   connect(inputLayer.y, outputLayer.u) annotation(
     Line(points = {{-48, 0}, {30, 0}}, color = {0, 0, 127}, thickness = 0.5));