Stabilization of desired periodic orbits in dynamic binary neural networks

Abstract

A dynamic binary neural network is a simple two-layer network with a delayed feedback and is able to generate various binary periodic orbits. The network is characterized by the signum activation function, ternary connection parameters, and integer threshold parameters. The ternary connection brings benefits to network hardware and to computation costs in numerical analysis. In order to stabilize a desired binary periodic orbit, a simple evolutionary algorithm is presented. The algorithm uses individuals corresponding to the ternary connection parameters and one zero element is inserted into each individual. Each individual is evaluated by two feature quantities that characterize the stability of the periodic orbit. The zero-insertion is able to reinforce the stability and is convenient to reduce power consumption in a hardware. Applying the algorithm to a class of periodic orbits, the stabilization capability is investigated. Some of the periodic orbits are applicable to control signals of switching power converters.