UKF-based remote state estimation for discrete artificial neural networks with communication bandwidth constraints

This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only partial components of the measurement outputs can be transmitted to the remote estimator at each time step. A UKF-based state estimator is developed to cope with the nonlinear activation functions in the neural networks subject to the communication constraints. Moreover, the stability of the proposed estimator is analyzed. Sufficient conditions are established under which the error dynamics of the state estimation is exponentially bounded in mean square. A numerical example is provided to demonstrate the effectiveness of the proposed method.

[1]  T. Tarn,et al.  Observers for nonlinear stochastic systems , 1975, 1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes.

[2]  D. Mandic,et al.  Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models , 2009 .

[3]  Guoqiang Hu,et al.  Distributed Covariance Intersection Fusion Estimation for Cyber-Physical Systems With Communication Constraints , 2016, IEEE Transactions on Automatic Control.

[4]  C. W. Chan,et al.  Performance evaluation of UKF-based nonlinear filtering , 2006, Autom..

[5]  Yan Liang,et al.  Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs , 2018, Inf. Fusion.

[6]  Peng Wang,et al.  Self-Taught Convolutional Neural Networks for Short Text Clustering , 2017, Neural Networks.

[7]  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.

[8]  İsmail Altın,et al.  Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network , 2018 .

[9]  M. Nazari,et al.  A novel enzyme based biosensor for catechol detection in water samples using artificial neural network , 2017 .

[10]  Jinde Cao,et al.  Impulsive Cohen–Grossberg BAM neural networks with mixed time-delays: An exponential stability analysis issue , 2018 .

[11]  Mohamed Boutayeb,et al.  A strong tracking extended Kalman observer for nonlinear discrete-time systems , 1999, IEEE Trans. Autom. Control..

[12]  Sandra M. Guzmán,et al.  The Use of NARX Neural Networks to Forecast Daily Groundwater Levels , 2017, Water Resources Management.

[13]  Yuanqing Xia,et al.  UKF-based nonlinear filtering over sensor networks with wireless fading channel , 2015, Inf. Sci..

[14]  Syed Mohammad Ashraf,et al.  Voltage stability monitoring of power systems using reduced network and artificial neural network , 2017 .

[15]  Woochang Lim,et al.  Stochastic spike synchronization in a small-world neural network with spike-timing-dependent plasticity , 2017, Neural Networks.

[16]  Subhrajit Roy,et al.  An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Zidong Wang,et al.  On Kalman-Consensus Filtering With Random Link Failures Over Sensor Networks , 2018, IEEE Transactions on Automatic Control.

[18]  Utpal Sarma,et al.  Prediction of Moisture Loss in Withering Process of Tea Manufacturing Using Artificial Neural Network , 2018, IEEE Transactions on Instrumentation and Measurement.

[19]  José J. Oliveira.  Global exponential stability of nonautonomous neural network models with unbounded delays , 2017, Neural Networks.

[20]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[21]  Yan Liang,et al.  Tobit Kalman Filter With Time-Correlated Multiplicative Sensor Noises Under Redundant Channel Transmission , 2017, IEEE Sensors Journal.

[22]  Gang Feng,et al.  Optimal linear estimation for networked systems with communication constraints , 2011, Autom..

[23]  Yuanqing Xia,et al.  Stochastic stability of a modified unscented Kalman filter with stochastic nonlinearities and multiple fading measurements , 2017, J. Frankl. Inst..

[24]  Yuanqing Xia,et al.  Unscented Kalman Filter Over Unreliable Communication Networks With Markovian Packet Dropouts , 2013, IEEE Transactions on Automatic Control.

[25]  Xian Zhang,et al.  Robust exponential passive filtering for uncertain neutral-type neural networks with time-varying mixed delays via Wirtinger-based integral inequality , 2017 .

[26]  Hamid Reza Karimi,et al.  Filtering of Discrete-Time Switched Neural Networks Ensuring Exponential Dissipative and $l_{2}$ – $l_{\infty }$ Performances , 2017, IEEE Transactions on Cybernetics.

[27]  M. Syed Ali,et al.  Finite-time stability for memristor based switched neural networks with time-varying delays via average dwell time approach , 2018, Neurocomputing.

[28]  Parisa Azimi,et al.  Use of artificial neural networks to decision making in patients with lumbar spinal canal stenosis. , 2017, Journal of neurosurgical sciences.

[29]  Anirban Roy,et al.  Unscented-Kalman-Filter-Based Multitarget Tracking Algorithms for Airborne Surveillance Application , 2016 .

[30]  Yuanqing Xia,et al.  Stochastic stability of the unscented Kalman filter with intermittent observations , 2012, Autom..

[31]  Fuad E. Alsaadi,et al.  H∞ state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through Fading channels , 2017, Neural Networks.

[32]  Adel M. Alimi,et al.  Stability and Exponential Synchronization of High-Order Hopfield Neural Networks with Mixed Delays , 2017, Cybern. Syst..

[33]  Ivanka M. Stamova,et al.  Mittag-Leffler synchronization of fractional neural networks with time-varying delays and reaction-diffusion terms using impulsive and linear controllers , 2017, Neural Networks.

[34]  K. J. Ray Liu,et al.  Distributed State Estimation With Dimension Reduction Preprocessing , 2014, IEEE Transactions on Signal Processing.

[35]  G. Giannakis,et al.  Kalman Filtering in Wireless Sensor Networks , 2010, IEEE Control Systems.

[36]  George Vukovich,et al.  Robust Adaptive Unscented Kalman Filter for Spacecraft Attitude Estimation Using Quaternion Measurements , 2017 .

[37]  E. Jury,et al.  ALMOST SURE BOUNDEDNESS OF RANDOMLY SAMPLED SYSTEMS , 1971 .