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Solving Combinatorial Optimization Problems Using the Oscillatory Neural Network
Yoshiaki WATANABE Keiichi YOSHINO Tetsuro KAKESHITA
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E80D
No.1
pp.7277 Publication Date: 1997/01/25
Online ISSN:
DOI:
Print ISSN: 09168532 Type of Manuscript: PAPER Category: BioCybernetics and Neurocomputing Keyword: neural network, optimization problem, NPcomplete,
Full Text: PDF>>
Summary:
The Hopfield neural network for optimization problems often falls into local minima. To escape from the local minima, the neuron unit in the neural network is modified to become an oscillatory unit by adding a simple selffeedback circuit. By combining the oscillatory unit with an energyvalue extraction circuit, an oscillatory neural network is constructed. The network can repeatedly extract solutions, and can simultaneously evaluate them. In this paper, the network is applied to four NPcomplete problems to demonstrate its generality and efficiency. The network can solve each problem and can obtain better solutions than the original Hopfield neural network and simple algorithms.

