Non-fragile synchronization of BAM neural networks with randomly occurring controller gain fluctuation

Ganesh Kumar Thakur; Sudesh Kumar Garg; Tej Singh; M. Syed Ali; Tarun Kumar Arora; Ganesh Kumar Thakur; Sudesh Kumar Garg; Tej Singh; M. Syed Ali; Tarun Kumar Arora

doi:10.3934/mbe.2023317

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 7302-7315. doi: 10.3934/mbe.2023317

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Non-fragile synchronization of BAM neural networks with randomly occurring controller gain fluctuation

1.
Department of Applied sciences, ABES Engineering College, Ghaziabad, UP-201007, India
2.
Department of Applied Science, G L BAJAJ Institute of Technology and Management, Greater Noida, Uttar Pradesh, India
3.
Department of Mathematics, Thiruvalluvar University, Vellore-632115, Tamilndau, Inida

Received: 03 August 2022 Revised: 04 December 2022 Accepted: 08 December 2022 Published: 14 February 2023

In this research, a non-fragile synchronization of bidirectional association memory (BAM) delayed neural networks is taken into consideration. The controller gain fluctuation seems in a very random manner, that obeys sure Bernoulli distributed noise sequences. Delay dependent criteria are derived to confirm the asymptotic stability of the BAM delayed neural networks. The non-fragile controller are often obtained by determination a collection of linear matrix inequalities (LMIs). A simulation example is used to demonstrate the efficiency of the developed control.
- non-fragile control,
- BAM neural network,
- time-varying delay,
- synchronization
Citation: Ganesh Kumar Thakur, Sudesh Kumar Garg, Tej Singh, M. Syed Ali, Tarun Kumar Arora. Non-fragile synchronization of BAM neural networks with randomly occurring controller gain fluctuation[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 7302-7315. doi: 10.3934/mbe.2023317

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Abstract

In this research, a non-fragile synchronization of bidirectional association memory (BAM) delayed neural networks is taken into consideration. The controller gain fluctuation seems in a very random manner, that obeys sure Bernoulli distributed noise sequences. Delay dependent criteria are derived to confirm the asymptotic stability of the BAM delayed neural networks. The non-fragile controller are often obtained by determination a collection of linear matrix inequalities (LMIs). A simulation example is used to demonstrate the efficiency of the developed control.

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