Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 24, Issue 6, Pages 868-877Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2012.2236352
Keywords
Contraction mapping theorem; delays; existence; impulsive control; periodic solution; recurrent neural networks; stability theory; uniqueness
Categories
Funding
- National Natural Science Foundation of China [61273233, 60834004, 11226136]
- Research Foundation for the Doctoral Program of Higher Education [20090002110035, 20120002110035]
- Project of Shandong Province Higher Educational Science and Technology Program [J12LI04]
- Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province [BS2012DX039]
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In this paper, a class of recurrent neural networks with discrete and continuously distributed delays is considered. Sufficient conditions for the existence, uniqueness, and global exponential stability of a periodic solution are obtained by using contraction mapping theorem and stability theory on impulsive functional differential equations. The proposed method, which differs from the existing results in the literature, shows that network models may admit a periodic solution which is globally exponentially stable via proper impulsive control strategies even if it is originally unstable or divergent. Two numerical examples and their computer simulations are offered to show the effectiveness of our new results.
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