The purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in controlsystems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation.The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. Care must be taken, when training perceptron networks, to ensure that they do not overfit the training data and then fail to generalize well in new situations.We have implemented the XOR logic using neural network tool in MATLAB and defined its use in control system.How system error can be used as feedback to the training network is also demonstrated in this paper.The paper gives a brief introspect into the neural network implimentation of control systems which in our case is the XOR network with its standard set of inputs and the respective standard set of outputs.

1. .Hagan, M. T., H.B. Demuth and M.H. Beale, Neural Network Design, PWS Publishing, Boston, 1996.
2. .Bishop, C., Neural Networks for Pattern Recognition, Oxford, New York, 1995.
3. .Haykin, S., Neural Networks: A Comprehensive Foundation, 2nd Ed., Prentice-Hall, Englewood Cliffs, NJ, 1999.
4. .www.library.cmu.edu

Perceptron Backpropagation algorithm Neuron model XOR logic Network Data Manager