This is a two input, one output neural network using back propagation algorithm. How can I modify it to be a 2 inputs, 2 hidden nodes, and 2 output nodes?

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.h file

#include  #include  #include  #define BP_LEARNING (float)(0.5) // The learning coefficient. class CBPNet { public: CBPNet(); ~CBPNet() {}; float Train(float, float, float); float Run(float, float); private: float m_fWeights[3][3]; // Weights for the 3 neurons. float Sigmoid(float); // The sigmoid function. }; CBPNet::CBPNet() { srand((unsigned)(time(NULL))); for (int i=0;i<3;i++) { for (int j=0;j<3;j++) { // For some reason, the Microsoft rand() function // generates a random integer. So, I divide by the // number by MAXINT/2, to get a num between 0 and 2, // the subtract one to get a num between -1 and 1. m_fWeights[i][j] = (float)(rand())/(32767/2) - 1; } } } float CBPNet::Train(float i1, float i2, float d) { float net1, net2, i3, i4, out; // Calculate the net values for the hidden layer neurons. net1 = 1 * m_fWeights[0][0] + i1 * m_fWeights[1][0] + i2 * m_fWeights[2][0]; net2 = 1 * m_fWeights[0][1] + i1 * m_fWeights[1][1] + i2 * m_fWeights[2][1]; // Use the hardlimiter function - the Sigmoid. i3 = Sigmoid(net1); i4 = Sigmoid(net2); // Now, calculate the net for the final output layer. net1 = 1 * m_fWeights[0][2] + i3 * m_fWeights[1][2] + i4 * m_fWeights[2][2]; out = Sigmoid(net1); // We have to calculate the deltas for the two layers. (thus the // name 'BACK-propagation'). float deltas[3]; deltas[2] = out*(1-out)*(d-out); deltas[1] = i4*(1-i4)*(m_fWeights[2][2])*(deltas[2]); deltas[0] = i3*(1-i3)*(m_fWeights[1][2])*(deltas[2]); // Now, alter the weights accordingly. float v1 = i1, v2 = i2; for(int i=0;i<3;i++) { // Change the values for the output layer, if necessary. if (i == 2) { v1 = i3; v2 = i4; } m_fWeights[0][i] += BP_LEARNING*1*deltas[i]; m_fWeights[1][i] += BP_LEARNING*v1*deltas[i]; m_fWeights[2][i] += BP_LEARNING*v2*deltas[i]; } return out; } float CBPNet::Sigmoid(float num) { return (float)(1/(1+exp(-num))); } float CBPNet::Run(float i1, float i2) { float net1, net2, i3, i4; net1 = 1 * m_fWeights[0][0] + i1 * m_fWeights[1][0] + i2 * m_fWeights[2][0]; net2 = 1 * m_fWeights[0][1] + i1 * m_fWeights[1][1] + i2 * m_fWeights[2][1]; i3 = Sigmoid(net1); i4 = Sigmoid(net2); net1 = 1 * m_fWeights[0][2] + i3 * m_fWeights[1][2] + i4 * m_fWeights[2][2]; return Sigmoid(net1); } 

========================================================= #include  #include "bpnet.h" using namespace std; #define BPM_ITER 2000 void main() { CBPNet bp; for (int i=0;i
                        

                            
                                                            

                                    

                                        

                                                                                    
                                        

                                                                                    
                                    
                                
                                                    

                        

                            
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