A Dual Neural Network for Solving Entropy-Maximising Models

Datos Bibliográficos

ID4138917
AutoresYee Leung (0000-0002-4140-5448, Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, and Joint Laboratory for Geoinformation Science, The Chinese University of Hong Kong, Hong Kong, autor de correspondencia), Xing-Bao Gao (Shaanxi Normal University), Xingbao Gao (0000-0002-3253-7529, Shaanxi Normal University, autor de correspondencia), Kai-Zhou Chen (0000-0002-2867-6825, Microelectronic Institute, Xidian University, Xi'an, Shaanxi 710071, People's Republic of China, autor de correspondencia)
Año2004
Volumen36
Número5
Páginas897-919
Fecha de publicación2004-05-01
Open AccessSí
TipoARTICLE
RevistaEnvironment and Planning A Economy and Space (JOURNAL)
Identificadores de la revistaISSN: 0308-518X • E-ISSN: 1472-3409
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1068/a3673a
OpenAlexW2087769242
IdiomaEN
Referencias citadas30

The entropy-maximixing model has been applied with varying degrees of success in the analysis and planning of origin - destination types of spatial interaction. Although theoretical underpinnings and solution methods have been developed over the years, there are still outstanding problems that need to be thoroughly investigated. From the practical point of view, solving this model directly and in real time has high theoretical and pragmatic value. In this paper we propose a neural network for solving the dual problem of this model in real time. The size of the proposed network is very small and its structure is very simple, so it can be implemented in hardware. From the theoretical perspective, we solve the seldom investigated issue of convergence to the optimal solution of the entropy-maximising model. We strictly prove that the proposed dual neural network is Lyapunov stable and that each of its trajectories can converge asymptotically to an exact solution of the dual problem. The validity and transient behaviour of the proposed neural network are demonstrated by numerical examples. It is also demonstrated that the proposed network approach renders for the first time a tight integration of an entropy-maximising model and a neural network, and offers a general representation and solution to a large variety of entropy-maximising models

Artificial neural network · Mathematical optimization · Computer Science · Evacuation and Crowd Dynamics · Mathematics · Transportation Planning and Optimization · Urban Transport and Accessibility · Artificial Intelligence

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