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Stability Analysis Of Two Kinds Of Neural Networks With Time Varying Delays Based On LMI

Posted on:2011-04-03Degree:MasterType:Thesis
Country:ChinaCandidate:X T JingFull Text:PDF
GTID:2178330332483428Subject:Applied Mathematics
Abstract/Summary:PDF Full Text Request
At present, the cell neural networks have been successfully applied to many kinds of fields such as signal processing, associative memory, pattern recognition, information and image processing, graphics identify, optimization, parallel computing, robotics and biological vision, partial differential equation solving, higher brain function, combinatorial optimization and so on, and their applications have made notable progress. The neural network stable aspect's research is very meaningful, special somewhat actual application request system is global robust stability or global exponential stability. This paper obtained some criterions of stability using the Lyapunov-Krasovskii function. This paper is divided into four chapters.Chapter 1 is introduction, and it mainly involves the proposition of neural networks and the significance of stability analysis and introduces some related neural network model the research results, the models of neural networks studied in this paper, the stability theory in dynamics and the main work in this paper.Chapter 2 uses the Lyapunov-Krasovskii stability theory, constructs the new Lyapunov-Krasovskii function and has studied the continual neural networks model with time varying delays, proposed delay dependent global exponential stability conditions. Finally three illustrative examples are given to demonstrate the effectiveness of the proposed results in numerical examples by matlab toolbox. Through carrying on the comparison with the existing results, it is confirmed the paper result is better than the existing results.Chapter 3 based on linear matrix inequality technique, a kind of discrete cellular neural networks with multiple time varying delays is studied. Delay dependent global exponential stability conditions are presented in this chapter by defining a new Lyapunov-Krasovskii functional. Finally the effectiveness of the proposed results in this chapter is demonstrated in numerical examples by matlab toolbox.Chapter 4 results in this paper are summarized. Stability problem of neural networks is also forecasted and point out the further research direction.
Keywords/Search Tags:Globally exponential stability, Jensen integral inequality, Linear matrix inequality (LMI), Homeomorphism mapping
PDF Full Text Request
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