ABSTRACT
A neural network is basically a model structure and an algorithm for fitting the model to some given data. The network approach to modeling a plant uses a generic nonlinearity and allows all the parameters to be adjusted. In this way it can deal with a wide range of nonlinearities. Learning is the procedure of training a neural network to represent the dynamics of a plant. The neural network is placed in parallel with the plant and the error h between the output of the system and the network outputs, the prediction error, is used as the training signal. Neural networks have a potential for intelligent control systems because they can learn and adapt, they can approximate nonlinear functions, they are suited for parallel and distributed processing, and they naturally model multivariable systems. If a physical model is unavailable or too expensive to develop, a neural network model might be an alternative. The sections in this chapter provide an understanding of the basic neural networks, their history, and the components of a neural network.
