Reachable Set Estimation for Memristive Complex-Valued Neural Networks With Disturbances

This brief focuses on reachable set estimation for memristive complex-valued neural networks (MCVNNs) with disturbances. Based on algebraic calculation and Gronwall-Bellman inequality, the states of MCVNNs with bounded input disturbances converge within a sphere. From this, the convergence speed is...

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Published in:IEEE transaction on neural networks and learning systems 2023-12, Vol.34 (12), p.11029-11034
Main Authors: Zhu, Song, Gao, Yu, Hou, Yuxin, Yang, Chunyu
Format: Article
Language:English
Subjects:
Bounded disturbances
complex-valued neural networks
Convergence
Delays
Disturbances
Estimation
Learning systems
memristor
Memristors
Neural networks
Observers
reachable set estimation
Switches
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Gao, Yu
Hou, Yuxin
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description This brief focuses on reachable set estimation for memristive complex-valued neural networks (MCVNNs) with disturbances. Based on algebraic calculation and Gronwall-Bellman inequality, the states of MCVNNs with bounded input disturbances converge within a sphere. From this, the convergence speed is also obtained. In addition, an observer for MCVNNs is designed. Two illustrative simulations are also given to show the effectiveness of the obtained conclusions.
doi_str_mv 10.1109/TNNLS.2022.3167117
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source IEEE Electronic Library (IEL) Journals
subjects Bounded disturbances
complex-valued neural networks
Convergence
Delays
Disturbances
Estimation
Learning systems
memristor
Memristors
Neural networks
Observers
reachable set estimation
Switches
title Reachable Set Estimation for Memristive Complex-Valued Neural Networks With Disturbances
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