Event-triggered H∞ state estimation for discrete-time delayed switched stochastic neural networks with persistent dwell-time switching regularities and sensor saturations

Partial-Neurons-Based Proportional-Integral Observer Design for Artificial Neural Networks: A Multiple Description Encoding Scheme

This article is concerned with a new partial-neurons-based proportional-integral observer (PIO) design problem for a class of artificial neural networks (ANNs) subject to bounded disturbances. For the purpose of improving the reliability of the data transmission, the multiple description encoding mechanisms are exploited to encode the measurement data into two identically important descriptions, and the encoded data are then transmitted to the decoders via two individual communication channels susceptible to packet dropouts, where Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the packet dropouts. An explicit relationship is discovered that quantifies the influences of the packet dropouts on the decoding accuracy, and a sufficient condition is provided to assess the boundedness of the estimation error dynamics. Furthermore, the desired PIO parameters are calculated by solving two optimization problems based on two metrics (i.e., the smallest ultimate bound and the fastest decay rate) characterizing the estimation performance. Finally, the applicability and advantage of the proposed PIO design strategy are verified by means of an illustrative example.

A resilient outlier-resistant recursive filtering approach to time-delayed spatial-temporal systems with energy harvesting sensors

This paper is concerned with a resilient outlier-resistant recursive filtering problem for a class of time-delayed spatial-temporal systems (STSs) with energy harvesting sensors. We consider a situation that the sensors are able to harvest energy from external environments and consume certain energy when the measurements are transmitted to filters. When the energy of the sensor is insufficient to maintain the normal communication between the sensors and the filters, the measurement will be regarded as missing. For the sake of obtaining a satisfactory filtering performance, the innovations influenced by the measurement outliers is constrained by introducing a saturation function in the filter. Furthermore, the resilient issue of the designed recursive filter is considered to resist the fluctuations of the filter parameters. Under the effects of sensor energy constraints, measurement outliers as well as parameter fluctuations, a resilient outlier-resistant recursive filter is designed where an upper bound (UB) is first obtained on the filtering error covariance (FEC). Then, by resorting to a matrix recursive equation, such a UB is minimized by the filter gain matrix. Finally, we exhibit a numerical example to verify the effectiveness of the proposed resilient outlier-resistant recursive filter scheme for time-delayed STSs.