DOI number:10.1109/TNNLS.2018.2793185
Affiliation of Author(s):广东工业大学自动化学院
Journal:IEEE Transactions on Neural Networks and Learning Systems
Key Words:dissipative estimator, distributed delays, Markov chain, neural networks, packet dropouts
Abstract:This paper investigates the estimator design for the neural networks, where distributed delays and imperfect measurements are included. A randomly occurred neuron-dependent nonlinearity is used to describe the uncertain measurements disturbed by neurons. The measurements are transmitted over multiple transmission channels, and Markov chains are introduced to model packet dropouts of these channels. A one-toone map is constructed to transform m independent Markov chains to an augmented one to facilitate system analysis. A new variable called channel state is defined based on the cases of packet dropouts, and the channel-state-dependent estimator is designed to trade off between the number and the performance of the estimator. Sufficient conditions are established to guarantee that the augmented system is stochastically stable and satisfies the strict (Q, S, R)-γ -dissipativity. The estimator gains are derived using linear matrix methods. Finally, an example is applied to illustrate the effectiveness of the developed methods.
First Author:xuyong
Indexed by:Journal paper
Discipline:Engineering
Document Type:J
Volume:29
Issue:10
Page Number:5149 - 5158
ISSN No.:29994373
Translation or Not:no
Date of Publication:2018-02-12
Links to published journals:https://ieeexplore.ieee.org/document/8290578