On Recognition Capacity of a Phase Neural Network

Abstract

The paper studies the properties of a fully connected neural network built around phase neurons. The signals traveling through the interconnections of the network are unit pulses with fixed phases. The phases encoding the components of associative memory vectors are distributed at random within the interval [0, 2π]. The simplest case in which the connection matrix is defined according to Hebbian learning rule is considered. The Chernov–Chebyshev technique, which is independent of the type of distribution of encoding phases, is used to evaluate the recognition error. The associative memory of this type of network is shown to be four times as large as that of a conventional Hopfield-type network using binary patterns. Correspondingly, the radius of the domain of attraction is also four times larger.