Identification of Nonlinear System with Feedback Structure
Many studies have been made in the identification of nonlinear system by Volterra-Wiener kernels based on the input-output data or the black box method. This pa-per is aimed to develop a method of identification that will reach into the internal structure of the system including feedback, where each one of the blocks can be individually identified. We shall first present a number of new theorems to model the nonlinear system into a system of Chapaullel-cascaded structure, as long as the system satisfies the Volterra-Wiener condition. This structure is then transformed into a number of different types of feedback structures which have nonlinear blocks either in the feedback or feedforward path. A sufficient condition for the convergence of these types of feedback system is given and a numerical example is presented to illustrate the validity of this method. Many physiological feedback systems that have adaptation behavior, such as the retinal cells or auditory nerve fibers, could be represented by this method. It would therefore greatly extend our knowledge in the understanding of the complex control dynamic structure of physiological systems, as demonstrated in its application to various sensory and neural systems.
KeywordsFeedback System Cascade Model Volterra Series Auditory Nerve Fiber Volterra Kernel
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