Abstract
The frequency assignment problem belongs to the quite difficult to deal with class of NP (nondeterministic polynomial) - hard combinatorial optimization problems [3]. Its computational complexity directs researchers in the field at developing efficient techniques for finding solutions realizing minimum (or maximum) values of an objective function subject to a set of, often conflicting, constraints. To seek an optimal (or near optimal) solution, many methods have been proposed, such as dynamic programming methods, branch and bound methods, etc., and, lately, some heuristic algorithms relating to physical and biological phenomena. They include tabu search, genetic algorithms, simulated annealing and artificial neural networks [4]. We propose a Hop-field neural network model with chaotic neurodynamics to overcome the obstacle of local minima in the energy function and obtain optimal solutions in less iterations than the time-consuming convergent dynamics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
D.H. Ackley, G.E. Hinton, and T.J. Sejnowski. A learning algorithm for Boltzmann machines. Cognitive Science, 9:147–169, 1985.
D.J. Amit, H. Gutfreund, and H. Sompolinsky. Spin-glass models for neural networks. Physical Review, A(32):1007–1018, 1986.
D.J. Castelino, S. Hurley, and N.M. Stephens. A tabu search algorithm for frequency assignment. Annals of Ops Res, 63:301–319, 1996.
Euclid cepa 6 project proposal. RTP 6.4 combinatorial algorithms for military applications, project specification. appendix 3, ftp://ftp.win.tue.nl/pub/techreports/CALMA/.
L. Clen. Application of chaotic simulation and self-organizing neural net to power system voltage stability monitoring. In Second Int. Forums on Applications of Neural Networks to Power Systems, 7B1. Yokohama, Japan, 1993.
L. Clen and K. Aihara. Chaotic Simulated Annealing for Combinatorial Optimization, volume 1, pages 319–322. 1994.
L. Clen and K. Aihara. Transient Chaotic Neural Networks and Chaotic Simulated Annealing, pages 347–352. 1994.
L. Clen and K. Aihara. Chaotic simulated annealing by a neural network model with transient chaos. Neural Networks, 8(6):915–930, 1995.
N. Funabiki and Y. Takefuji. A neural network parallel algorithm for channel assignment problems in cellular radio networks. IEEE Trans. Veh. Technol., 41(4):430–437, 1992.
A. Gamst and W. Rave. On frequency assignment in mobile automatic telephone systems. In Proc. GLOBECOM’ 82 1982, pages 57–64, May 1978.
J.J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. In Proceedings of the National Academy of Sciences’ 79, pages 2554–2558, 1982.
J.J. Hopfield and D.W. Tank. Neural computation of decisions in optimization problems. Biological Cybernetics, 52:141–152, 1985.
T. Kasahara and M. Nakagawa. Parameter-controlled chaos neural networks. Electronics and Communications in Japan, Part 3, 78(7), 1995.
Y. Takefuji and K.C. Lee. Artificial neural networks for four-coloring map problems and k-colorability problems. IEEE Trans. Circuit systems, 38(3):326–333, March 1991.
J. Tani. Proposal of chaotic steepnest descent method for neural networks and analysis of their dynamics. Trans. Inst. Electron. Inf. Commun., J74-A-8:1208–1215, 1991.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Wien
About this paper
Cite this paper
Dorkofikis, K., Stephens, N.M. (1998). Chaotic Neurodynamics in the Frequency Assignment Problem. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_69
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6492-1_69
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive