Selection of neural network structures: some approximation theory guidelines

Abstract

Control engineers have not been slow in making use of recent developments in artificial neural networks: a pioneering paper was written by Narendra and Partnasarathy and more recent developments are surveyed in this book. Neural networks allow many of the ideas of system identification and adaptive control originally applied to linear (or linearised) systems to be generalised, so as to cope with more severe nonlinearities. Such strong nonlinearities occur in a number of applications e.g. in robotics or process control. Two possible schemes for 'direct' adaptive and 'indirect' adaptive control are shown and other schemes will be found elsewhere in this book, but in this chapter we shall concentrate on the modelling to be carried out by the artificial neural networks.