Abstract
In this paper, we focus on the global stability analysis with respect to dynamical delayed neural networks (NNs) that contain parameter uncertainties. Many investigations on the sufficient conditions utilizing different upper bounds for the norm of interconnection matrices pertaining to the global asymptotic robust stability of delayed NNs have been conducted. In this study, a new upper bound of the norm of connection weight matrices is derived for the delayed NNs under parameter uncertainties. The key focus is on how the new upper bound is able to yield minimum result with respects to some of the existing upper bounds. We demonstrate that the new upper bound can lead to some new sufficient conditions with respect to the global asymptotic robust stability of equilibrium point of the delayed NNs. The slope bounded activation functions and Lyapunov-Krasovskii functionals (LKFs) are employed for formulating the sufficient conditions of the equilibrium point of NNs. Moreover, the derived sufficient conditions are independent on the time delay parameter. Numerical examples are provided and the outcomes obtained are compared with those of the existing results subject to different network parameters.
Highlights
In recent years, the role of neural network (NN) has been significantly developed due to their successful applications to different areas
Our study is significant because different upper bounds play a major role in the determination of the sufficient conditions pertaining to the global robust stability of dynamical delayed NN models
Through this new upper bound, we are able to formulate the sufficient conditions with respect to the global asymptotically robust stability (GARS) of delayed NN models
Summary
The role of neural network (NN) has been significantly developed due to their successful applications to different areas. It is possible to examine the range of network parameters even in the presence of incomplete information In this regard, by using the interval theory of NN connection weight matrices, we can identify the upper bounds with respect to the norm of interval matrices. Our study is significant because different upper bounds play a major role in the determination of the sufficient conditions pertaining to the global robust stability of dynamical delayed NN models. Through this new upper bound, we are able to formulate the sufficient conditions with respect to the GARS of delayed NN models.
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