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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2008, Vol. 9 Issue (2): 262-270    DOI: 10.1631/jzus.A071336
Electrical & Electronic Engineering     
Exponential synchronization of general chaotic delayed neural networks via hybrid feedback
Mei-qin LIU, Jian-hai ZHANG
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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Abstract  This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, recurrent multilayer perceptrons (RMLPs). By virtue of Lyapunov-Krasovskii stability theory and linear matrix inequality (LMI) technique, some exponential synchronization criteria are derived. Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

Key words： Exponential synchronization      Hybrid feedback      Drive-response conception      Linear matrix inequality (LMI)      Chaotic neural network model     
Received: 23 June 2007      Published: 10 January 2008
CLC:  TP183  
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Mei-qin LIU
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Mei-qin LIU, Jian-hai ZHANG. Exponential synchronization of general chaotic delayed neural networks via hybrid feedback. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(2): 262-270.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A071336     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2008/V9/I2/262

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