Nonfragile Synchronization of Semi-Markovian Jumping Neural Networks with Time Delays via Sampled-Data Control and Application to Chaotic Systems

K Sivaranjani,M Sivakumar,K Loganathan,S Dharani,Ngawang Ngmgyel,Ndolane Sene

doi:10.1155/2021/2562227

K Sivaranjani, M Sivakumar + Show 4 more

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https://doi.org/10.1155/2021/2562227

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This study discusses the synchronization problem for delayed neural networks with semi-Markovian jumping parameters. With the support of Jenson’s inequality and Writinger-based integral inequality, a suitable Lyapunov–Krasovskii functional was constructed, and a synchronization criterion for the considered system was derived in the form of LMIs. In order to cope with system uncertainties, the nonfragile controller was taken into account. Also, sampled-data controller is used to improve the effectiveness of the bandwidth usage. In order to achieve the benefits of both control techniques, the nonfragile sampled-data controller was considered for synchronization of semi-Markovian jumping neural networks and to assure that the error system is asymptotically stable. At last, numerical simulations are exhibited to validate the proposed technique.

Highlights

Due to their effective implementation in cryptography, image analysis, associative memory, model identification, and so on, more attention has been given to different neural network (NN) models over the past centuries [1,2,3,4]
Different from the previous literature, in this work, we employed the novel control technique, namely, nonfragile sampled-data control which includes the benefits of both control techniques for synchronization of SMJNNs
The synchronization of SMJNNs has been analyzed through the hybrid control technique, namely, nonfragile sampled-data control

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Introduction

Due to their effective implementation in cryptography, image analysis, associative memory, model identification, and so on, more attention has been given to different neural network (NN) models over the past centuries [1,2,3,4]. Synchronization problem for coupled inertial neural networks with reaction diffusion terms and time-varying delays via pinning sampled-data control has been considered in [5]. Event-triggered synchronization problem for semi-Markovian jumping complex dynamical networks with a reliable control technique has been investigated in [19]. The designed controller should be able to tolerate some uncertainties in its coefficients because uncertainty cannot be avoided for many reasons, such as the inherent imprecision in analog systems and additional parameter tuning in the final implementation of the controller Due to this fact, the nonfragile controller has been studied by many researchers [30,31,32]. Nonfragile synchronization for chaotic time-delay neural networks with semi-Markovian jump parameters has been discussed in [32]. (iv) numerical simulations are presented to validate the correctness of the proposed control technique

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