Direct adaptive control of nonlinear systems using a dynamic neural network

In this paper the development and application of a dynamic neural network called the dynamic neural processor (DNP) are discussed. The DNP is developed based on the neuro-physiological evidence that neural activity of any complexity depends upon the interaction of assembly of cells called neural subpopulations. A single subpopulation of neurons is assumed to consist of a number of similar neurons that lie in close proximity. For simplicity, live neural subpopulations, namely excitatory and inhibitory, are assumed to exist in a neural population. The DNP consists of two dynamic neurons configured to function as excitatory and inhibitory neurons. A learning algorithm to adapt the weights of the subpopulation and the connecting weights between the subpopulations is developed. The efficacy of the DNP structure is demonstrated for the direct adaptive control of unknown nonlinear systems