Set partition of real numbers by hopfield neural network

Yutaka Hisanaga, Masafumi Yamashita, Tadashi Ae

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The Hopfield neural network is attracting attention as a high‐speed solver for optimization and other problems. However, it contains a problem in that the network may not arrive at the globally optimal solution but stops at a locally optimal solution unless the initial state is selected appropriately. In other words, the selection of the initial state is important in solving a problem using the Hopfield neural network. This paper considers the real number partition problem, the problem of finding the k largest elements from a given set of real numbers, given integer k. A network to solve this problem is constructed. It is shown that the network arrives at the globally optimal solution if the initial state satisfies a certain condition. Its basin also is examined. As an application example of the network, the sorting problem is considered.

Original languageEnglish
Pages (from-to)88-95
Number of pages8
JournalSystems and Computers in Japan
Volume22
Issue number10
DOIs
Publication statusPublished - Jan 1 1991

Fingerprint

Set Partition
Hopfield neural networks
Hopfield Neural Network
Sorting
Optimal Solution
High Speed
Partition
Integer
Optimization

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture
  • Computational Theory and Mathematics

Cite this

Set partition of real numbers by hopfield neural network. / Hisanaga, Yutaka; Yamashita, Masafumi; Ae, Tadashi.

In: Systems and Computers in Japan, Vol. 22, No. 10, 01.01.1991, p. 88-95.

Research output: Contribution to journalArticle

Hisanaga, Yutaka ; Yamashita, Masafumi ; Ae, Tadashi. / Set partition of real numbers by hopfield neural network. In: Systems and Computers in Japan. 1991 ; Vol. 22, No. 10. pp. 88-95.
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