A Collective Neurodynamic System for Distributed Optimization with Applications in Model Predictive Control

Le, X; Yan, Z; Xi, J

A Collective Neurodynamic System for Distributed Optimization with Applications in Model Predictive Control

Le, X Yan, Z

Xi, J

Permalink

Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Emerging Topics in Computational Intelligence, 2017, 1, (4), pp. 305-314
Issue Date:: 2017-08-01

Closed Access

	Filename	Description	Size
	A_Collective_Neurodynamic_System_for_Distributed_Optimization_with_Applications_in_Model_Predictive_Control.pdf		1.33 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Le, X
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100
dc.contributor.author	Xi, J
dc.date.accessioned	2022-07-12T06:37:07Z
dc.date.available	2022-07-12T06:37:07Z
dc.date.issued	2017-08-01
dc.identifier.citation	IEEE Transactions on Emerging Topics in Computational Intelligence, 2017, 1, (4), pp. 305-314
dc.identifier.issn	2471-285X
dc.identifier.issn	2471-285X
dc.identifier.uri	http://hdl.handle.net/10453/158810
dc.description.abstract	A collective neurodynamic system is presented to distributed convex optimization subject to linear equality and box constraints in framework of an autonomous multiagent network. The overall objective to be minimized takes an additive form of multiple local objective functions. Agents in the system, each of which is modeled by a recurrent neural network, cooperatively, and autonomously develop their dynamic behaviors based on real-time interactions with their neighbors. In specific, each recurrent neural network has knowledge on a local objective only with no access to the overall objective function. It exchanges neuronal state with neighboring agents via a predefined communication topology. Guided by their individual neurodynamics and the collective efforts, all agents reach consensus at the global optimal solution to the distributed convex optimization. Illustrative examples are provided to substantiate the theoretical properties. A case study on model predictive control is reported to further validate the collective neurodynamic system.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Emerging Topics in Computational Intelligence
dc.relation.isbasedon	10.1109/TETCI.2017.2716377
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Collective Neurodynamic System for Distributed Optimization with Applications in Model Predictive Control
dc.type	Journal Article
utslib.citation.volume	1
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	2022-07-12T06:37:04Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	1
utslib.citation.issue	4

Abstract:

A collective neurodynamic system is presented to distributed convex optimization subject to linear equality and box constraints in framework of an autonomous multiagent network. The overall objective to be minimized takes an additive form of multiple local objective functions. Agents in the system, each of which is modeled by a recurrent neural network, cooperatively, and autonomously develop their dynamic behaviors based on real-time interactions with their neighbors. In specific, each recurrent neural network has knowledge on a local objective only with no access to the overall objective function. It exchanges neuronal state with neighboring agents via a predefined communication topology. Guided by their individual neurodynamics and the collective efforts, all agents reach consensus at the global optimal solution to the distributed convex optimization. Illustrative examples are provided to substantiate the theoretical properties. A case study on model predictive control is reported to further validate the collective neurodynamic system.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/158810