scholarly journals Sliding Intermittent Control for BAM Neural Networks with Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Jianqiang Hu ◽  
Jinling Liang ◽  
Hamid Reza Karimi ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Control Method ◽  
Impulsive Control ◽  
Intermittent Control ◽  
Bam Neural Networks ◽  
Periodically Intermittent Control ◽  
Control Scheme ◽  
Special Cases ◽  
Exponentially Stable ◽  
Continuous Feedback

This paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM) neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results.

scholarly journals Exponential Stabilization of Coupled Hybrid Stochastic Delayed BAM Neural Networks: A Periodically Intermittent Control Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Yunjian Peng ◽  
Birong Zhao ◽  
Weijie Sun ◽  
Feiqi Deng
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Control Method ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Functional Method ◽  
Exponential Stabilization ◽  
Intermittent Control ◽  
Analysis Techniques ◽  
Basic Parameters

This paper considers exponential stabilization for a class of coupled hybrid stochastic delayed bidirectional associative memory neural networks (HSD-BAM-NN) with reaction-diffusion terms. A periodically intermittent controller is proposed to exponentially stabilize such an unstable HSD-BAM-NN, and sufficient conditions of the closed-loop BAM-NN system with exponential stabilization are derived by using Lyapunov-Krasovskii functional method, stochastic analysis techniques, and integral inequality property, which decide the basic parameters of the proposed controller. Furthermore, a framework to establish simulation algorithm with sampled states is presented to implement the stabilization controller. With a HSD-BAM-NN model of power synchronization in a photovoltaic (PV) array field, we illustrate numerical simulation results to verify the correctness and effectiveness of the proposed controller.

SYNCHRONIZATION IN AN ARRAY OF NONLINEARLY COUPLED CHAOTIC NEURAL NETWORKS WITH DELAY COUPLING

2008 ◽  
Vol 18 (10) ◽  
pp. 3101-3111 ◽  
Author(s):  
JIANQUAN LU ◽  
DANIEL W. C. HO ◽  
JINDE CAO
Keyword(s):  
Neural Networks ◽  
Nearest Neighbor ◽  
Chaotic Neural Networks ◽  
Scale Free ◽  
Dynamical Network ◽  
Feedback Strength ◽  
Control Scheme ◽  
Special Cases ◽  
General Complex ◽  
Free Network

A general complex dynamical network consisting of N nonlinearly coupled identical chaotic neural networks with coupling delays is firstly formulated. Many studied models with coupling systems are special cases of this model. Synchronization in such dynamical network is considered. Based on the Lyapunov–Krasovskii stability theorem, some simple controllers with updated feedback strength are introduced to make the network synchronized. The update gain γi can be properly chosen to make some important nodes synchronized quicker or slower than the rest. Two examples including nearest-neighbor coupled networks and scale-free network are given to verify the validity and effectiveness of the proposed control scheme.

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