This paper studies the simple dynamic binary neural network characterized by the signum activation function, ternary weighting parameters and integer threshold parameters. The network can be regarded as a digital version of the recurrent neural network and can output a variety of binary periodic orbits. The network dynamics can be simplified into a return map, from a set of lattice points, to itself. In order to store a desired periodic orbit, we present two learning algorithms based on the correlation learning and the genetic algorithm. The algorithms are applied to three examples: a periodic orbit corresponding to the switching signal of the dc-ac inverter and artificial periodic orbit. Using the return map, we have investigated the storage of the periodic orbits and stability of the stored periodic orbits.

This letter studies application of the growing PSO to the design of DC-AC inverters. In this application, each particle corresponds to a set of circuit parameters and moves to solve a multi-objective problem of the total harmonic distortion and desired average power. The problem is described by the hybrid fitness consisting of analog objective function, criterion and digital logic. The PSO has growing structure and dynamic acceleration parameters. Performing basic numerical experiments, we have confirmed the algorithm efficiency.