Bounds of the Induced Norm and Model Reduction Errors for a Class of Nonlinear Systems
نویسنده
چکیده
The class of nonlinear systems described by a discrete-time state equation containing a diagonal nonlinear term as in recurrent neural networks is considered. Suf-cient conditions are derived for the stability and induced norm of such systems using positive deenite diagonally dominant Lyapunov functions or storage functions , satisfying appropriate Linear Matrix Inequalities. Preliminary results are also presented for model reduction errors for such systems.
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