Open Access

Cellular Neural Networks for NP-Hard Optimization

EURASIP Journal on Advances in Signal Processing20092009:646975

Received: 24 September 2008

Accepted: 26 November 2008

Published: 18 January 2009


A cellular neural/nonlinear network (CNN) is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a two-dimensional Ising-type (Edwards-Anderson) spin-glass system. Using the properties of CNN, we show that one single operation (template) always yields a local minimum of the spin-glass energy function. This way, a very fast optimization method, similar to simulated annealing, can be built. Estimating the simulation time needed on CNN-based computers, and comparing it with the time needed on normal digital computers using the simulated annealing algorithm, the results are astonishing. CNN computers could be faster than digital computers already at 10 × 10 lattice sizes. The local control of the template parameters was already partially realized on some of the hardwares, we think this study could further motivate their development in this direction.


Neural NetworkInformation TechnologyLocal MinimumSimulated AnnealingLocal Control

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Authors’ Affiliations

Department of Physics, University of Notre Dame, Notre Dame, USA
Faculty of Information Technology, Péter Pázmany Catholic University, Budapest, Hungary
Computer and Automation Research Institute, Hungarian Academy of Sciences (MTA-SZTAKI), Budapest, Hungary
Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, Romania


© Mária Ercsey-Ravasz et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.