Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer SciencesVol.E89-ANo.1pp.354-358 Publication Date: 2006/01/01 Online ISSN: 1745-1337 DOI: 10.1093/ietfec/e89-a.1.354 Print ISSN: 0916-8508 Type of Manuscript: LETTER Category: Neural Networks and Bioengineering Keyword: bipartite subgraph problem, Hopfield neural network, hill-shift learning, NP-complete problem,
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Summary: In this paper, we present a hill-shift learning method of the Hopfield neural network for bipartite subgraph problem. The method uses the Hopfield neural network to get a near-maximum bipartite subgraph, and shifts the local minimum of energy function by adjusts the balance between two terms in the energy function to help the network escape from the state of the near-maximum bipartite subgraph to the state of the maximum bipartite subgraph or better one. A large number of instances are simulated to verify the proposed method with the simulation results showing that the solution quality is superior to that of best existing parallel algorithm.