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Neural adaptive observer for general nonlinear systems

Addresses the problem of nonlinear adaptive observer design for unknown general multivariable nonlinear systems. Only mild assumptions on the system are imposed; the output equation is at least C/sub l/; and existence and uniqueness of solution for the state equation. The proposed observer uses linearly parameterized neural networks whose weights are adaptively adjusted, and Lyapunov theory is used in order to guarantee stability for state estimation and NN weight errors. No strictly positive real assumption on the output error equation is required for the construction of the proposed observer.