Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control

Self Cite

The exponential stabilization problem for a class of time-varying delayed complex-valued memristor-based neural networks via discontinuous impulsive control is investigated in this paper. Firstly, the time-varying delayed complex-valued memristor-based neural networks is translated to real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed to guarantee exponential stabilization of the complex-valued memristor-based neural networks with time-varying delays. Thirdly, by constructing an appropriate Lyapunov Krasovskii functional and adopting linear matrix inequality (LMI) technique, some sufficient exponential stabilization criteria are derived. Finally, an illustrative example is provided to illustrate the effectiveness of the obtained theoretical results.

“…Proof. Firstly, we will prove that u(t) = 0 is an equilibrium point of the model (12). According to Assumption 2, we know that F 1 (0) = 0, F 2 (0) = 0, G 1 (0) = 0, G 2 (0) = 0, and…”

Section: Resultsmentioning

confidence: 97%

Exponential Stabilization of Time‐varying Delayed Complex‐valued Memristor‐based Neural Networks Via Impulsive Control

Fang

et al. 2018

Self Cite

“…As for kinematic models, they generate curvilinear speed profiles reflecting the effect of neuromuscular impulses involved in the generation of motions. Many models have been developed under this approach such as the deltalognormal [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], the double gaussian [30], the sigma-lognormal [31], the double beta [32]. The problem of kinematic models is the lack of information on the spatial aspect of the movement.…”

Section: Overview Of Some Handwriting Modelsmentioning

confidence: 99%

Conventional and non conventional body motions modelling and control. Application to the handwriting process

Abdelkrim

Benrejeb

2019

The handwriting process is one of the most important and complex activities of the human body. In recent years, modelling and analyzing and controlling it have received great attention leading to large important motion studies results. This paper attempts to present an overview of recent works about our proposed approaches and other ones to model and control this process. Hence, an overall model of the handwriting process has been proposed and developed by the coauthors, showing the interest of using the soft computing concepts, namely neural networks and fuzzy logic. The design of this model requires several undertaken steps. Starting from an experimental approach to acquire electromyographic signals and writing positions, different conventional and non conventional models have been proposed. Besides some control strategies have been considered in particular neural and fuzzy approaches in addition to non conventional neural and neuro-fuzzy Internal Model Control strategy. The proposed global model, inspired from the biological structure system, has been designed and validated through simulation results.

“…In recent years, neural networks (NNs) have attracted increasing attention due to the wide applications in many areas, such as secure communication, automatic control, pattern recognition . Particularly, synchronization and control of coupled NNs have been widely investigated in various research areas .…”

Section: Introductionmentioning

confidence: 99%

Finite‐Time Synchronization of Complex‐Valued Delayed Neural Networks with Discontinuous Activations

Yang

et al. 2018

In this paper, finite-time synchronization for a class of complex-valued neural networks with time delays and discontinuous activations is concerned under the framework of Filippov solutions. Both state feedback and adaptive controllers are designed to overcome the difficulties in dealing with the time delay and the uncertainties of Filippov solutions. Based on the concept of Filippov solutions, differential inclusion and structuring suitable Lyapunov-Krasovskii functionals, some sufficient conditions are obtained to guarantee that a controlled response system is synchronized with a driving system in a finite time. Finally, numerical simulations are given to show the effectiveness of the theoretical results.