DocumentCode :
2225085
Title :
A generalized model of statistical Hopfield neural network to solve TSP
Author :
Kim, Yoo-Shin ; Cha, Seon-geun ; Kim, Ja-myeong
Author_Institution :
Pusan Nat. Univ., South Korea
fYear :
1997
fDate :
9-12 Sep 1997
Firstpage :
693
Abstract :
The travelling salesman problem (TSP) is the well-known NP-complete problem and many researchers have tried to solve it, but no method can solve this problem completely. One of the most dominant method is the statistical Hopfield (1982, 1984) neural network. We propose a generalized model which overcomes the weak points of Van Den Bout (1989) and Park´s (1991) method. We improve the energy function and consider all kinds of perturbation effects and the ratio of these effects. Through a simulation for a randomly generated distribution of 10 city, we found that our proposed model shows that 90 out of 100 cases reach the optimum and a near optimum solution within a 5% error
Keywords :
Hopfield neural nets; random processes; simulated annealing; statistical analysis; travelling salesman problems; NP-complete problem; TSP; critical temperature; energy function; generalized model; optimum solution; perturbation effects; randomly generated distribution; ratio; simulated annealing; simulation; statistical Hopfield neural network; travelling salesman problem; Boltzmann equation; Cities and towns; Cooling; Hopfield neural networks; NP-complete problem; Neural networks; Polynomials; Simulated annealing; Temperature;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
Print_ISBN :
0-7803-3676-3
Type :
conf
DOI :
10.1109/ICICS.1997.652066
Filename :
652066
Link To Document :
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