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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Bibliographic Details
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
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Summary: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.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2022.3167117