Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information

The paper investigates the state estimation problem for a class of recurrent neural networks with sampled-data information and time-varying delays. The main purpose is to estimate the neuron states through output sampled measurement; a novel event-triggered scheme is proposed, which can lead to a significant reduction of the information communication burden in the network; the feature of this scheme is that whether or not the sampled data should be transmitted is determined by the current sampled data and the error between the current sampled data and the latest transmitted data. By using a delayed-input approach, the error dynamic system is equivalent to a dynamic system with two different time-varying delays. Based on the Lyapunov-krasovskii functional approach, a state estimator of the considered neural networks can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed event-triggered scheme

H∞ State Estimation for BAM Neural Networks With Binary Mode Switching and Distributed Leakage Delays Under Periodic Scheduling Protocol

Research and Development Office Ministry of Education Kingdom of Saudi Arabia (Grant Number: HIQI-2-2019); National Natural Science Foundation of China (Grant Number: 61903254)

Dissipativity analysis for discrete time-delay fuzzy neural networks with Markovian jumps

This paper is concerned with the dissipativity analysis and design of discrete Markovian jumping neural networks with sector-bounded nonlinear activation functions and time-varying delays represented by Takagi–Sugeno fuzzy model. The augmented fuzzy neural networks with Markovian jumps are first constructed based on estimator of Luenberger observer type. Then, applying piecewise Lyapunov–Krasovskii functional approach and stochastic analysis technique, a sufficient condition is provided to guarantee that the augmented fuzzy jump neural networks are stochastically dissipative. Moreover, a less conservative criterion is established to solve the dissipative state estimation problem by using matrix decomposition approach. Furthermore, to reduce the computational complexity of the algorithm, a dissipative estimator is designed to ensure stochastic dissipativity of the error fuzzy jump neural networks. As a special case, we have also considered the mixed H∞ and passive analysis of fuzzy jump neural networks. All criteria can be formulated in terms of linear matrix inequalities. Finally, two examples are given to show the effectiveness and potential of the new design techniques.Yingqi Zhang, Peng Shi, Ramesh K. Agarwal, and Yan Sh

Finite-Time State Estimation for Delayed Neural Networks with Redundant Delayed Channels

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61703245 and 61873148); 10.13039/501100010029-Taishan Scholar Project of Shandong Province of China; 10.13039/501100002858-China Post-Doctoral Science Foundation (Grant Number: 2016M600547); Qingdao Post-Doctoral Applied Research Project (Grant Number: 2016117); Post-Doctoral Special Innovation Foundation of Shandong (Grant Number: 201701015); 10.13039/501100000288-Royal Society of the U.K.; 10.13039/100005156-Alexander von Humboldt Foundation of German