Spiking neural networks for identification and control of dynamic plants

Abstract

In this paper a Spiking Neural Networks (SNN)-based model is developed for identification and control of dynamic plants. Spike Response Model (SRM) has been employed to design the model. The learning of the parameters of SNN is carried out using a gradient algorithm. For its use for identification and control purposes, a coding is applied to convert real numbers into spikes. The SNN structure is tested for the identification and control of the dynamic plants commonly used in the literature. It has been found that the proposed structure results in a good performance despite its smaller parameter space.