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A neural network decoupling strategy for a class on nonlinear discrete time systems. (Chinese. English summary) Zbl 0939.93011

Authors’ abstract: “A neural network is considered to be used as a compensator for input-output decoupling of a class of nonlinear discrete-time systems. A necessary and sufficient condition for the solvability of the decoupling problem for the class of discrete time systems is given. It is also shown that if the decoupling problem is solvable, the modified systems can be linear and the poles of the modified systems can be freely assigned. Based on this result, a strategy for realizing decoupling via neural networks is proposed. Simulation results supports our theory and the decoupling strategy proposed in this paper”.

MSC:

93B51 Design techniques (robust design, computer-aided design, etc.)
92B20 Neural networks for/in biological studies, artificial life and related topics
93B55 Pole and zero placement problems
93C55 Discrete-time control/observation systems
93C10 Nonlinear systems in control theory
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