Partial-neurons-based state estimation for artificial neural networks under constrained bit rate: The finite-time case

This paper is concerned with the partial-neuron-based finite-time state estimation problem for a class of artificial neural networks with time-varying delays. Measurements information from only a small fractional of the artificial neurons are applied to the state estimation process. The data transmi...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 488; pp. 144 - 153
Main Authors: Wang, Licheng, Zhao, Di, Wang, Yu-Ang, Ding, Derui, Liu, Hongjian
Format: Journal Article
Language:English
Published: Elsevier B.V 01.06.2022
Subjects:
Artificial neural networks
Bit-rate constraints
Encoding–decoding mechanism
Finite-time state estimation
Partial-nodes-based state estimation
Encoding–decoding mechanism
Partial-nodes-based state estimation
Finite-time state estimation
Bit-rate constraints
Artificial neural networks
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary:This paper is concerned with the partial-neuron-based finite-time state estimation problem for a class of artificial neural networks with time-varying delays. Measurements information from only a small fractional of the artificial neurons are applied to the state estimation process. The data transmission from the sensor to estimator is implemented via a bit-rate constrained communication channel, and a data encoding–decoding scheme is developed to convert the original analog sensor measurements into certain digital codewords with fewer occupations of the network bandwidth. With the help of the Lyapunov stability theory, sufficient conditions are presented to guarantee the finite-time boundedness of the estimation error and the estimator gain matrix is parameterized in terms of the solution to certain matrix inequalities. Finally, a numerical example is provided to further confirm the effectiveness of the proposed state estimation scheme.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2022.03.001