Stability Analysis of Stochastic Markovian Jump Neural Networks with Different Time Scales and Randomly Occurred Nonlinearities Based on Delay-Partitioning Projection Approach | Zendy

Jianmin Duan | Zendy; Manfeng Hu | Zendy; Yongqing Yang | Zendy; Liuxiao Guo | Zendy

Open Access

Stability Analysis of Stochastic Markovian Jump Neural Networks with Different Time Scales and Randomly Occurred Nonlinearities Based on Delay-Partitioning Projection Approach

Author(s) -

Jianmin Duan,

Manfeng Hu,

Yongqing Yang,

Liuxiao Guo

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/212469

Subject(s) - mathematics , stability (learning theory) , projection (relational algebra) , artificial neural network , linear matrix inequality , exponential stability , control theory (sociology) , jump , stochastic neural network , nonlinear system , mathematical optimization , algorithm , recurrent neural network , computer science , physics , control (management) , quantum mechanics , machine learning , artificial intelligence

In this paper, the mean square asymptotic stability of stochastic Markovian jump neural networks with different time scales and randomly occurred nonlinearities is investigated. In terms of linear matrix inequality (LMI) approach and delay-partitioning projection technique, delay-dependent stability criteria are derived for the considered neural networks for cases with or without the information of the delay rates via new Lyapunov-Krasovskii functionals. We also obtain that the thinner the delay is partitioned, the more obviously the conservatism can be reduced. An example with simulation results is given to show the effectiveness of the proposed approach

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