Partial-Neurons-Based Passivity-Guaranteed State Estimation for Neural Networks With Randomly Occurring Time Delays

In this brief, the partial-neurons-based passivity-guaranteed state estimation (SE) problem is examined for a class of discrete-time artificial neural networks with randomly occurring time delays. The measurement outputs available utilized for the SE are allowed to be available only at a fraction of neurons in the networks. A Bernoulli-distributed random variable is employed to characterize the random nature of the occurrence of time delays. By resorting to the Lyapunov–Krasovskii functional method as well as the stochastic analysis technique, sufficient criteria are provided for the existence of the desired state estimators ensuring the estimation error dynamics to achieve the asymptotic stability in the mean square with a guaranteed passivity performance level. In addition, the parameterization of the estimator gain is acquired by solving a convex optimization problem. Finally, the validity of the obtained theoretical results is illustrated via a numerical simulation example.

[1]  Yonggang Chen,et al.  Exponential Synchronization for Delayed Dynamical Networks via Intermittent Control: Dealing With Actuator Saturations , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Dan Zhang,et al.  Mixed H∞ and passivity based state estimation for fuzzy neural networks with Markovian-type estimator gain change , 2014, Neurocomputing.

[3]  Rathinasamy Sakthivel,et al.  Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation , 2018, Neural Networks.

[4]  Hamid Reza Koofigar,et al.  Adaptive robust maximum power point tracking control for perturbed photovoltaic systems with output voltage estimation. , 2016, ISA transactions.

[5]  Lei Zou,et al.  Finite-Time State Estimation for Delayed Neural Networks With Redundant Delayed Channels , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  Hao Shen,et al.  Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Zidong Wang,et al.  State Estimation for Coupled Uncertain Stochastic Networks With Missing Measurements and Time-Varying Delays: The Discrete-Time Case , 2009, IEEE Transactions on Neural Networks.

[8]  Sabri Arik,et al.  An analysis of exponential stability of delayed neural networks with time varying delays , 2004, Neural Networks.

[9]  Fuad E. Alsaadi,et al.  Delay-distribution-dependent H∞ state estimation for delayed neural networks with (x, v)-dependent noises and fading channels , 2016, Neural Networks.

[10]  Huijun Gao,et al.  New passivity results for uncertain discrete-time stochastic neural networks with mixed time delays , 2010, Neurocomputing.

[11]  Vladimir L. Kharitonov,et al.  Stability of Time-Delay Systems , 2003, Control Engineering.

[12]  Jinde Cao,et al.  Robust State Estimation for Uncertain Neural Networks With Time-Varying Delay , 2008, IEEE Transactions on Neural Networks.

[13]  Wei Xing Zheng,et al.  Passivity-based sliding mode control of uncertain singular time-delay systems , 2009, Autom..

[14]  Huijun Gao,et al.  On H-infinity Estimation of Randomly Occurring Faults for A Class of Nonlinear Time-Varying Systems With Fading Channels , 2016, IEEE Transactions on Automatic Control.

[15]  Fuad E. Alsaadi,et al.  Finite-Time State Estimation for Recurrent Delayed Neural Networks With Component-Based Event-Triggering Protocol , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Fuad E. Alsaadi,et al.  Stochastic stability for distributed delay neural networks via augmented Lyapunov-Krasovskii functionals , 2018, Appl. Math. Comput..

[17]  Jinde Cao,et al.  Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism , 2016, Neural Networks.

[18]  Fuad E. Alsaadi,et al.  Partial-Nodes-Based State Estimation for Complex Networks With Unbounded Distributed Delays , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Rathinasamy Sakthivel,et al.  Combined H∞ and passivity state estimation of memristive neural networks with random gain fluctuations , 2015, Neurocomputing.

[20]  Qing-Long Han,et al.  State Estimation for Static Neural Networks With Time-Varying Delays Based on an Improved Reciprocally Convex Inequality , 2018, IEEE Transactions on Neural Networks and Learning Systems.