From the point of view of abstraction all you need to know is that this is a backpropagation neural network; you present it with an array of normalized numbers (normalized in the range -1 to 1) that describes the input or information about the 'situation'. It then tries to figure out what it is suppoused to output based on this input. You then tell it what it was suppoused to output by giving it an an array of normalized numbers (-1 to 1) that describe the decision or output The meaning of the numbers is up to you, it could represent normalized pixel values or air temperature. If there is any statistical correlation between what you input and what you want outputted the network will find it. Note: in the future there should be a momentum variable built into this so it does not get stuck in local optima.

Here is an example of how you might use this


Backpropagation_Neural_Network b;	//declare backpropagation network class
int * Init_Info = new int[4];		//make an array for startup info

Init_Info[0] = 3;		//there are 3 layers in the network we want
Init_Info[1] = 2;		//input layer has 2 neurons
Init_Info[2] = 3;		//hidden layer has 3 neurons
Init_Info[3] = 2;		//output layer has 2 neuron

//init the network, starting it with the array of network 
//info and a learning rate of 0.01
b.Init(Init_Info,0.01)		

bool Finished_Learning = false; //flag

double * In = new double[2];	//input layer has 2 neurons
double * Out = new double[2];	//output layer has 2 neurons

while (!(Finished_Learning))
{
	//fill the arrays with training data
	Fill_Normalized_Input_And_Output_Vectors(In,Out);
	//forward propagate, backpropagate and apply weight adjustments
	b.Train_Once(In,Out);								
}