DocumentCode :
3254478
Title :
Solving combinatorial optimization problems by nonlinear neural dynamics
Author :
Hasegawa, Mikio ; Ikeguchi, Tohru ; Matozaki, Takeshi ; Aihara, Kazuyuki
Author_Institution :
Dept. of Appl. Electron., Sci. Univ. of Tokyo, Japan
Volume :
6
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
3140
Abstract :
The new approach for combinatorial optimization problems using chaotic dynamics is discussed. We show effectiveness of chaotic neuro dynamics for solving combinatorial optimization problems by applying the chaotic neural network to traveling salesman problems. In this paper, we adopt the chaotic neural network model with two internal states, corresponding to mutual interactions which minimize an energy function and refractoriness which induce chaotic dynamics. We investigate relationships between solving abilities and different model parameters such as decay parameters of two internal states, Lyapunov exponents and first order statistics of firing patterns
Keywords :
chaos; combinatorial mathematics; neural nets; optimisation; Lyapunov exponents; chaotic dynamics; chaotic dynamics induction; chaotic neural network model; combinatorial optimization problems; decay parameters; energy function minimization; firing patterns; first-order statistics; mutual interactions; nonlinear neural dynamics; refractoriness; traveling salesman problems; Associative memory; Chaos; Electronics industry; Industrial electronics; Neural networks; Neurons; Power system dynamics; Power system simulation; Statistics; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.487286
Filename :
487286
Link To Document :
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