Less conservative results of state estimation for delayed neural networks with fewer LMI variables

Authors:
Cheng-De Zheng;Mingming Ma;Zhanshan Wang
Affiliations:
Department of Mathematics, Dalian Jiaotong University, 116028, China;Department of Mathematics, Dalian Jiaotong University, 116028, China;School of Information Science and Engineering, Northeastern University, 110004, China
Venue:
Neurocomputing
Year:
2011

Citing 19
Cited 3

Stability of Time-Delay Systems

Stability of Time-Delay Systems
Letters: Exponential state estimation for recurrent neural networks with distributed delays

Neurocomputing
An LMI approach to delay-dependent state estimation for delayed neural networks

Neurocomputing
State estimation for jumping recurrent neural networks with discrete and distributed delays

Neural Networks
Novel delay-dependent criteria for global robust exponential stability of delayed cellular neural networks with norm-bounded uncertainties

Neurocomputing
Neural networks letter: Stability analysis of static recurrent neural networks using delay-partitioning and projection

Neural Networks
New exponentially convergent state estimation method for delayed neural networks

Neurocomputing
New globally asymptotic stability criteria for delayed cellular neural networks

IEEE Transactions on Circuits and Systems II: Express Briefs
New Lyapunov-Krasovskii functionals for global asymptotic stability of delayed neural networks

IEEE Transactions on Neural Networks
State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: the discrete-time case

IEEE Transactions on Neural Networks
A scaling parameter approach to delay-dependent state estimation of delayed neural networks

IEEE Transactions on Circuits and Systems II: Express Briefs
State estimation for neural networks with mixed interval time-varying delays

Neurocomputing
Less conservative results of state estimation for neural networks with time-varying delay

Neurocomputing
Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay

IEEE Transactions on Neural Networks
Exponential stability on stochastic neural networks with discrete interval and distributed delays

IEEE Transactions on Neural Networks
Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
State estimation for delayed neural networks

IEEE Transactions on Neural Networks
Delay-dependent state estimation for delayed neural networks

IEEE Transactions on Neural Networks
Global Asymptotic Stability of Delayed Cellular Neural Networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, the state estimation problem is investigated for neural networks with time-varying delays as well as general activation functions. By applying the Finsler's Lemma and constructing appropriate Lyapunov-Krasovskii functional based on delay partitioning, several improved delay-dependent conditions are developed to estimate the neuron state with some available output measurements such that the error-state system is global asymptotically stable. It is established theoretically that one special case of the obtained criteria is equivalent to some existing result with same conservatism but including fewer LMI variables. As the present conditions involve no free-weighting matrices, the computational burden is largely reduced. Two examples are provided to demonstrate the effectiveness of the theoretical results.