D Gorse and J G Taylor

Neural Network World, 1, 193-202, 1991

A generalisation of the concept of binary-input stochastic learning automata is given which incorporates non-linearity and stochasticity to a maximal degree. This universal automaton is identified with the 'probabilistic random access memory' (pRAM), a hardware-realisable neural model previously proposed by the authors. A reinforcement training rule is presented for such automata, and convergence theorems proved. The nature of the invariant measure is explored for a 1-input automaton with a two dimensional state space. The reinforcement rule is then simulated in the context of a particular classification task, and the results compared favourably with those obtained by Barto and Anandan using a less general training rule.