Dynamic binary neural networks with time-variant parameters and switching of desired periodic orbits

Abstract

This paper studies 3-layer dynamic binary neural networks characterized by binary connection parameters and the signum activation function. The connection parameters and the number of hidden neurons are time-variant. The dynamics is described by a non-autonomous difference equation of binary state variables and the network can generate various periodic orbits of binary vectors. First, we present a simple synthesis method that guarantees storage of desired multiple periodic orbits and switching of the periodic orbits through desired entrances. Second, as a basic step to engineering applications, we present an FPGA based hardware prototype. The hardware can realize switching of various periodic orbits experimentally and typical examples are demonstrated. The switching of periodic orbits is applicable to switching of walking patterns in robotics and variable output of switching power converters.