Dynamic Neural Units with Applications to the Control of Unknown Nonlinear Systems

Article type: Research Article

Authors: Gupta, M.M. | Rao, D.H.

Affiliations: Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon S7N 0W0, Canada

Abstract: To emulate higher cognitive functions, such as sensory information processing and the attendant complexities of learning, memory storage, control, and vision, the biological neuron has to be modeled based upon feedback networks. The authors proposed such a neuronal model, called a “dynamic neural unit” (DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. The DNU architecture embodies delay elements, feedforward and feedback synaptic weights, and a nonlinear activation function. In this article, the concept of DNU is extended by incorporating both synaptic and somatic operations. The synaptic operation provides the optimum feed forward and feedback weights, while the somatic operation determines the optimum gain (slope) of the nonlinear activation function for a given task. The architectural details and the learning and adaptive algorithm to modify adjustable parameters, namely, feed forward and feedback synaptic weights, and slope of nonlinear function of the DNU are presented. The algorithm implementation of an isolated DNU is given. Considering DNU as the basic processing element, a three-stage dynamic neural network is developed. The effectiveness of the proposed dynamic neural network architecture as applied to the control of unknown nonlinear systems is discussed and extensive simulation results are presented.

DOI: 10.3233/IFS-1993-1108

Journal: Journal of Intelligent and Fuzzy Systems, vol. 1, no. 1, pp. 73-92, 1993