An improved neural network for manufacturing cell formation

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

With structures inspired by the structure of the human brain and nervous system, neural networks provide a unique computational architecture for addressing problems that are difficult or impossible to solve with traditional methods. In this paper, an unsupervised neural network model, based upon the interactive activation and competition (IAC) learning paradigm, is proposed as a good alternative decision-support tool to solve the cell-formation problem of cellular manufacturing. The proposed implementation is easy to use and can simultaneously form part families and machine cells, which is very difficult or impossible to achieve by conventional methods. Our computational experience shows that the procedure is fairly efficient and robust, and it can consistently produce good clustering results.

Original languageEnglish (US)
Pages (from-to)279-295
Number of pages17
JournalDecision Support Systems
Volume20
Issue number4
DOIs
StatePublished - Aug 1997

Fingerprint

Cellular manufacturing
Neural networks
Neurology
Brain
Neural Networks (Computer)
Chemical activation
Nervous System
Cluster Analysis
Learning
Cell formation
Manufacturing
Computational
Neural Networks
Cells
Clustering
Paradigm
Activation
Cell formation problem
Network model
Decision support

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

Cite this

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An improved neural network for manufacturing cell formation. / Chu, Chao Hsien.

In: Decision Support Systems, Vol. 20, No. 4, 08.1997, p. 279-295.

Research output: Contribution to journalArticle

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