channels called connections. The general structure of the network that have been used here is given in Fig.1. the structure consists of three layers : the input layer, the hidden layer and the output layer. The input layer is made up of one or more neurons or processing elements that collectively represent the information in a particular pattern of a training set. The hidden layer also consists of one or more neurons. Its purpose is simply to transform the information from the input layer to prepare it for the output layer. The output layer, which has one or more neurons, uses input from the hidden layer (which is a transformation of the input layer) to produce an output value for the entire network. The output is used to interpret the training and classification results of the network. The processing elements or neurons are connected to each other by adjustable weights. The input/output behaviour of the network changes if the weights are changed. So, the weights of the net may be chosen in such a way so as to achieve a desired output. To satisfy this goal, systematic ways of adjusting the weights have to be developed, which are known as training or learning algorithm. 

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