Please use this identifier to cite or link to this item:
|Title:||Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties|
|Keywords:||Discrete-time neural networks;State estimation;Time-delayed neural networks;Non-fragile state estimator;Uncertain systems|
|Abstract:||This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays. The norm-bounded parameter uncertainties enter into all the system matrices, and the network output is of a general type that contains both linear and nonlinear parts. The additive variation of the estimator gain is taken into account that reflects the possible implementation error of the neuron state estimator. The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties. Sufficient conditions are presented to guarantee the existence of the desired non-fragile state estimators by using the Lyapunov stability theory and the explicit expression of the desired estimators is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design approach.|
|Appears in Collections:||Dept of Computer Science Research Papers|
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.