Energy-to-Peak State Estimation for Markov Jump RNNs With Time-Varying Delays via Nonsynchronous Filter With Nonstationary Mode Transitions
Authors:
- 6 January 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 26 (10) , 2346-2356
Abstract
In this paper, the problem of energy-to-peak state estimation for a class of discrete-time Markov jump recurrent neural networks (RNNs) with randomly occurring nonlinearities (RONs) and time-varying delays is investigated. A practical phenomenon of nonsynchronous jumps between RNNs modes and desired mode-dependent filters is considered, and a nonstationary mode transition among the filters is used to model the nonsynchronous jumps to different degrees that are also mode dependent. The RONs are used to model a class of sector-like nonlinearities that occur in a probabilistic way according to a Bernoulli sequence. The time-varying delays are supposed to be mode dependent and unknown, but with known lower and upper bounds a priori. Sufficient conditions on the existence of the nonsynchronous filters are obtained such that the filtering error system is stochastically stable and achieves a prescribed energy-to-peak performance index. Further to the recent study on the class of nonsynchronous estimation problem, a monotonicity is observed in obtaining filtering performance index, while changing the degree of nonsynchronous jumps. A numerical example is presented to verify the theoretical findings.Keywords
Funding Information
- Fundamental Research Funds for the Central Universities, China (HIT.BRETIII.201211, HIT.BRETIV.201306)
- National Natural Science Foundation of China (61021002, 61322301)
- Australian Research Council (DP120104986)
- Deanship of Scientific Research (DSR), King Abdulaziz University (KAU) (81-130-35-HiCi)
This publication has 34 references indexed in Scilit:
- Multivariate neural network operators with sigmoidal activation functionsNeural Networks, 2013
- Exponential filtering for distributed delay systems with Markovian jumping parametersSignal Processing, 2013
- Distributed ${\cal H}_{\infty}$ Filtering for a Class of Markovian Jump Nonlinear Time-Delay Systems Over Lossy Sensor NetworksIEEE Transactions on Industrial Electronics, 2012
- Synchronization of Coupled Neutral-Type Neural Networks With Jumping-Mode-Dependent Discrete and Unbounded Distributed DelaysIEEE Transactions on Cybernetics, 2012
- Delay-Slope-Dependent Stability Results of Recurrent Neural NetworksIEEE Transactions on Neural Networks, 2011
- Delay-Dependent $H_{\infty}$ and Generalized $H_{2}$ Filtering for Delayed Neural NetworksIEEE Transactions on Circuits and Systems I: Regular Papers, 2008
- Design of filter for Markov jumping linear systems with non-accessible mode informationAutomatica, 2008
- Global exponential stability of generalized recurrent neural networks with discrete and distributed delaysNeural Networks, 2006
- RobustH∞ filtering for switched linear discrete-time systems with polytopic uncertaintiesInternational Journal of Adaptive Control and Signal Processing, 2006
- Markovian Architectural Bias of Recurrent Neural NetworksIEEE Transactions on Neural Networks, 2004