Neural Network Controllers

Abstract

Neural networks are developed by morphologically and computationally simulating a human brain. Although, as seen in previous chapters, the precise operation details of artificial neural networks are quite different from human brains, they are similar in three aspects. First, a neural network consists of a very large number of simple processing elements (the neurons). Second, each neuron is connected to a large number of other neurons. Third, the functionality of the networks is determined by modifying the strengths of connections during a learning phase [Psaltis et al, 1988; Hunt et al, 1992; Warwick et ai, 1992]. Efforts have been made to find efficient approaches for control from physiological studies of the brain. Research over the last twenty years has revealed the architecture and performance characteristics of the brain as a controller [Albus, 1975; Ito, 1984; Kawato et al, 1987] and has shown that neural network controllers have important advantages over conventional controllers. The first advantage is that a neural network controller can efficiently utilise a much larger amount of sensory information in planning and executing a control action than an industrial controller can. The second advantage is that a neural network controller has the collective processing capability that enables it to respond quickly to complex sensory inputs while the execution speed of sophisticated control algorithms in a conventional controller is severely limited.