Synchronization control of a class of memristor-based recurrent neural networks

Wu, Ailong; Wen, Shiping; Zeng, Zhigang

doi:10.1016/j.ins.2011.07.044

Cited by 350 publications

(123 citation statements)

References 39 publications

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“…In addition, it is known that Cohen-Grossberg neural network includes some well-known neural networks such as Hopfield neural networks, cellular neural networks and recurrent neural networks as a special case. From this point, we can conclude that our results are more practical than those in , Wu et al (2012), Wen et al (2013), Li and Cao (2015), , Zhang and Shen (2014).…”

Section: \0;mentioning

confidence: 51%

“…In Wu et al (2012), Wen et al (2013), Li and Cao (2015), Shen (2013, 2014), based on the theory of differential inclusions, the authors studied the complete synchronization of various types of memristor-based cellular neural networks. In this paper, for the special case aðtÞ ¼ À1, the anti-synchronization can be achieved, and for the special case aðtÞ ¼ 1, the complete synchronization can be achieved.…”

Section: \0;mentioning

confidence: 99%

“…Because of the unpredictability of the scaling function aðtÞ in this function projective synchronization can additionally enhance the security of communication than other complete synchronization or antisynchronization papers (Yang et al 2014;Wu et al 2012;Wen et al 2013;Li and Cao 2015;Shen 2013, 2014). From this point, our results are superior and have greater applicability.…”

Section: \0;mentioning

confidence: 99%

“…As a result, Cohen-Grossberg neural networks have attracted considerable research attention and many good results related have been reported on the boundedness, stability, convergence, synchronization and other oscillatory properties of Cohen-Grossberg neural networks (Cao and Liang 2004;Lisena 2011;Zhu and Cao 2010;. In addition, it has been reported that the memristorbased neural networks can exhibit some complicated dynamics and even chaotic behavior, and synchronization of chaotic neural networks based on memristor has also become an important area of study (Yang et al 2014;Wu et al 2012;Wen et al 2013;Li and Cao 2015;Shen 2013, 2014;Bao and Cao 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In , the antisynchronization of memristive recurrent neural networks with time-varying delays was studied by using differential inclusions theory and Lyapunov functional method. In Wu et al (2012), Wen et al (2013), Li and Cao (2015), , Zhang and Shen (2014), based on the theory of differential equations with discontinuous righthand side as introduced by Filippov, the authors investigated the complete synchronization of various types of memristor-based neural networks with or without delays. In , the finite-time synchronization of memristor-based neural networks was studied by using finite time stability theory and differential inclusions theory.…”

Section: Introductionmentioning

confidence: 99%

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Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

2015

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This paper deals with the problem of function projective synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying delays. Based on the theory of differential equations with discontinuous right-hand side, some novel criteria are obtained to realize the function projective synchronization of addressed networks by combining open loop control and linear feedback control. As some special cases, several control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization of the considered memristor-based CohenGrossberg neural network. Finally, a numerical example and its simulations are provided to demonstrate the effectiveness of the obtained results.

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Section: \0;mentioning

confidence: 51%

Section: \0;mentioning

confidence: 99%

Section: \0;mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

2015

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Finite‐time synchronization and adaptive synchronization of memristive recurrent neural networks with delays

Fang

2018

Adaptive Control & Signal

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SummaryThis paper solves the finite‐time synchronization and adaptive synchronization problems of drive‐response memristive recurrent neural networks with delays under two control methods. First, the state‐feedback control rule containing delays and the adaptive control rule are designed for realizing synchronization of drive‐response memristive recurrent neural networks in finite time. Then, on the basis of the Lyapunov stability theory, many algebraic sufficient conditions are obtained to guarantee finite‐time synchronization and adaptive synchronization of drive‐response memristive recurrent neural networks via two control methods, which are easily verified. In addition, the estimation of the upper bounds of the settling time of finite‐time synchronization is obtained. Lastly, to illustrate the effectiveness of the obtained theoretical results, two examples are given.

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Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays

Ang

2020

Advanced Intelligent Systems

121

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Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to overcome the intrinsic energy and speed issues of traditional von Neumann based computing architecture. With the ability to perform vector‐matrix multiplications and flexible tunable conductance, the memristor crossbar array (CBA) structure is one of the most promising candidates to realize neural cognitive systems. The boom in the development of memristive synapses and neurons has propelled the developments of artificial neural networks (ANNs) to emulate the highly hierarchically organized network of human brain in the past decade. To achieve this, realizing large scale, high‐density memristive CBAs is a prerequisite to constructing complex ANNs. Herein, the stringent requirements in device performance and array parameters for hardware ANNs are analyzed, and the efforts in addressing the associated challenges are discussed. Recent progress on the experimental demonstration of neuromorphic computing systems (NCSs) is presented. Recommendations for further performance optimization at the device, circuit, and algorithm levels are proposed. This Report serves as a guide for the hardware implementation of NCS based on large‐scale CBAs.

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Synchronization control of a class of memristor-based recurrent neural networks

Cited by 350 publications

References 39 publications

Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

Finite‐time synchronization and adaptive synchronization of memristive recurrent neural networks with delays

Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays

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