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Pratap Anbalagan,Raja Ramachandran,Jinde Cao,Grienggrai Rajchakit,Chee Peng Lim 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.2
In this article, we explore the theoretical issues on the drive-response synchronization of a class of fractionalorder uncertain complex valued neural networks (FOUCNNs) with mixed time varying delays and impulses. Based upon the contraction mapping principle, robust analysis techniques, as well as Riemann-Liouville (R-L)derivative, we derive a new set of novel sufficient conditions for the existence and uniqueness of equilibrium pointof such neural network system, while by applying the Lyapunov functional approach, the global stability of theequilibrium solutions are obtained. Furthermore, the synchronization criterion of FOUCNNs is also attracted bymeans of the adaptive error feedback control strategy. Finally, two examples are provided along with the simulationresults to demonstrate the effectiveness of our main proofs.
Maharajan Chinnamuniyandi,Raja Ramachandran,Jinde Cao,Grienggrai Rajchakit,Xiaodi Li 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.2
This paper mainly focuses on a novel H∞ control design to handle the global robust exponential stability problem for uncertain stochastic neutral-type neural networks (USNNNs) with mixed time-varying delays. Here the delays are assumed to be both discrete and distributed, which means that the lower and upper bounds can be derived. Firstly, we draw a control law for stabilized and stability of the neutral-type neural networks (NNNs). Secondly, by employing the Lyapunov-Krasovskii functional(LKF) theory, Jensen’s integral inequality, new required sufficient conditions for the global robust exponential stability of the given neural networks (NNs) are established in terms of delay-dependent linear matrix inequalities (LMIs), which can be easily checked in practice. The conditions obtained are expressed in terms of LMIs whose feasibility can be verified easily by MATLAB LMI control toolbox. Moreover, we have compared our work with previous one in the existing literature and showed that it reduces conservatism. Finally, one numerical example with their simulations is given to validate the effectiveness of our proposed theoretical results.
Sowmiya Chandran,Raja Ramachandran,Jinde Cao,Ravi P. Agarwal,Grienggrai Rajchakit 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.8
This proposed research work is devoted to the problem of passivity analysis for uncertain BAM neural networks with leakage, discrete and distributed delays using novel summation inequality. The uniqueness of this proposal is from the deliberation of an advanced inequality (i.e., novel summation inequality) which is novelty than the famous Jensen inequality engaged in the framework of discrete-time neural networks systems. For the investigation of continuous-time neural networks, Wirtinger-based integral inequality was currently engaged whereas novel summation inequality is for discrete-time neural networks. With the assistance of Lyapunov-Krasovskii functional, some conditions are derived and entrenched in terms of linear matrix inequalities which can be easily checked by some available software packages. Two benchmark examples are proposed and lead better upper bounds for the tolerable time delay than existing literatures to show the fruitfulness and efficacy of the proposed work.