A mixed analog/digital chaotic neuro-computer system for quadratic assignment problems

Yoshihiko Horio, Tohru Ikeguchi, Kazuyuki Aihara

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

We construct a mixed analog/digital chaotic neuro-computer prototype system for quadratic assignment problems (QAPs). The QAP is one of the difficult NP-hard problems, and includes several real-world applications. Chaotic neural networks have been used to solve combinatorial optimization problems through chaotic search dynamics, which efficiently searches optimal or near optimal solutions. However, preliminary experiments have shown that, although it obtained good feasible solutions, the Hopfield-type chaotic neuro-computer hardware system could not obtain the optimal solution of the QAP. Therefore, in the present study, we improve the system performance by adopting a solution construction method, which constructs a feasible solution using the analog internal state values of the chaotic neurons at each iteration. In order to include the construction method into our hardware, we install a multi-channel analog-to-digital conversion system to observe the internal states of the chaotic neurons. We show experimentally that a great improvement in the system performance over the original Hopfield-type chaotic neuro-computer is obtained. That is, we obtain the optimal solution for the size-10 QAP in less than 1000 iterations. In addition, we propose a guideline for parameter tuning of the chaotic neuro-computer system according to the observation of the internal states of several chaotic neurons in the network.

Original languageEnglish
Pages (from-to)505-513
Number of pages9
JournalNeural Networks
Volume18
Issue number5-6
DOIs
Publication statusPublished - 2005 Jul 1
Externally publishedYes

Keywords

  • Chaotic neural networks
  • Mixed analog/digital system
  • Quadratic assignment problem

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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