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Less Conservative Stability Criteria for a Class of Nonlinear Stochastic Hopfield Neural Networks with Time-varying Delays
- Source :
- Applied Mathematics & Information Sciences. 7:131-137
- Publication Year :
- 2013
- Publisher :
- Natural Sciences Publishing, 2013.
-
Abstract
- In this paper, the problem of stability analysis of nonlinear stochastic Hopfield neural networks(FHNNs) with time-varying delays is investigated by using the Takagi-Sugeno(T-S) approach. Combined with both the fuzzy relaxed technique and an improved free-weighting matrix approach with weighting-dependent Lagrange multipliers, less conservative stability criteria is proposed via the Lyapunov-Krasovskii functional approach. Furthermore, related algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis and the obtained stability criteria is in terms of Linear matrix inequalities. Finally, an illustrative example shows less conservatism of the proposed approaches.
- Subjects :
- Numerical Analysis
Artificial neural network
Computer science
Applied Mathematics
Stability (learning theory)
Fuzzy logic
Computer Science Applications
Hopfield network
Matrix (mathematics)
symbols.namesake
Nonlinear system
Computational Theory and Mathematics
Computer Science::Systems and Control
Control theory
Lagrange multiplier
symbols
Applied mathematics
Stochastic neural network
Analysis
Subjects
Details
- ISSN :
- 23250399 and 19350090
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Applied Mathematics & Information Sciences
- Accession number :
- edsair.doi...........32fc9e97a9fa19ac10273f8df36571b6