The backpropagation technique is basically an extension of the adaline. The backpropagation network is a multilayer perceptron model with an input layer, one or more hidden layers, and an output layer. These layers are organized into interconnected layers, allowing the backpropagation network to escape the adaline's linear separability limitations making this network much more powerful. This approach was documented by Werbos (1974). The backpropagation network is used for problems that involve classification, projection, interpretation, and generalization. The nodes in the backpropagation neural network are interconnected via weighted links with each node usually connecting to the next layer up till the output layer which provides output for the network. The input patterns values are presented and assigned to the input nodes of the input layer. The input values are initialized to values between -1.1 and 1.1. The nodes in the next layer receive the input values through links and compute output values of their own, which are then passed to the next layer. These values propagate forward through the layers till the output layer is reached, or put another way, till each output layer node has produced an output value for the network. The desired output for the input pattern is used to compute an error value for each node in the output layer, and then propagated backwards (and here's where the network name comes in) through the network as the delta rule is used to adjust the link values to produce better the desired output. Once the error produced by the patterns in the training set is below a given tolerance, the training is complete and the network is presented new input patterns and produce an output based on the experience it gained from the learning process.