AN AUTOMATA-THEORETICAL APPROACH TO DEVELOPING LEARNING NEURAL NETWORKS
L.P.J. Veelenturf · Cybernetics & Systems · 1981
A formal automata-theoretical model for learning neural networks is given. The networks may grow while they learn. It is demonstrated that a neural network can be described as an automaton. Two extreme learning procedures are presented as boundaries for potential learning strategies. An example of a fairly simple automata-theoretical learning procedure is given and modifications suggested by neurophysiological data are incorporated to improve the speed of convergence.