Multistability and associative memory of neural networks with Morita-like activation functions.

Shen, Y; Zhu, S; Liu, X; Wen, S

Multistability and associative memory of neural networks with Morita-like activation functions.

Shen, Y Zhu, S Liu, X Wen, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Neural Networks, 2021, 142, pp. 162-170
Issue Date:: 2021-10

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Field	Value	Language
dc.contributor.author	Shen, Y
dc.contributor.author	Zhu, S
dc.contributor.author	Liu, X
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2022-04-27T04:43:58Z
dc.date.available	2021-04-26
dc.date.available	2022-04-27T04:43:58Z
dc.date.issued	2021-10
dc.identifier.citation	Neural Networks, 2021, 142, pp. 162-170
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/156650
dc.description.abstract	This paper presents the multistability analysis and associative memory of neural networks (NNs) with Morita-like activation functions. In order to seek larger memory capacity, this paper proposes Morita-like activation functions. In a weakened condition, this paper shows that the NNs with n-neurons have (2m+1)n equilibrium points (Eps) and (m+1)n of them are locally exponentially stable, where the parameter m depends on the Morita-like activation functions, called Morita parameter. Also the attraction basins are estimated based on the state space partition. Moreover, this paper applies these NNs into associative memories (AMs). Compared with the previous related works, the number of Eps and AM's memory capacity are extensively increased. The simulation results are illustrated and some reliable associative memories examples are shown at the end of this paper.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Neural Networks
dc.relation.isbasedon	10.1016/j.neunet.2021.04.035
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Neurons
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Neurons
dc.subject.mesh	Neurons
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.title	Multistability and associative memory of neural networks with Morita-like activation functions.
dc.type	Journal Article
utslib.citation.volume	142
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-27T04:43:56Z
pubs.publication-status	Published
pubs.volume	142

Abstract:

This paper presents the multistability analysis and associative memory of neural networks (NNs) with Morita-like activation functions. In order to seek larger memory capacity, this paper proposes Morita-like activation functions. In a weakened condition, this paper shows that the NNs with n-neurons have (2m+1)n equilibrium points (Eps) and (m+1)n of them are locally exponentially stable, where the parameter m depends on the Morita-like activation functions, called Morita parameter. Also the attraction basins are estimated based on the state space partition. Moreover, this paper applies these NNs into associative memories (AMs). Compared with the previous related works, the number of Eps and AM's memory capacity are extensively increased. The simulation results are illustrated and some reliable associative memories examples are shown at the end of this paper.

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http://hdl.handle.net/10453/156650