IEEE Trans Neural Netw Learn Syst - Further result on guaranteed H8 performance state estimation of delayed static neural networks.

Tópicos

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Resumo

This brief considers the guaranteed H8 performance state estimation problem of delayed static neural networks. An Arcak-type state estimator, which is more general than the widely adopted Luenberger-type one, is chosen to tackle this issue. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with a prescribed H8 performance. It is shown that the design of suitable gain matrices and the optimal performance index are accomplished by solving a convex optimization problem subject to two linear matrix inequalities. Compared with some previous results, much better performance is achieved by our approach, which is greatly benefited from introducing an additional gain matrix in the domain of activation function. An example is finally given to demonstrate the advantage of the developed result.

Resumo Limpo

brief consid guarante h perform state estim problem delay static neural network arcaktyp state estim general wide adopt luenbergertyp one chosen tackl issu delaydepend criterion deriv estim error system global asymptot stabl prescrib h perform shown design suitabl gain matric optim perform index accomplish solv convex optim problem subject two linear matrix inequ compar previous result much better perform achiev approach great benefit introduc addit gain matrix domain activ function exampl final given demonstr advantag develop result

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