UNIK-OPT/NN neural network based adaptive optimal controller on optimization models

Wooju Kim, Jae K. Lee

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

When the future information for an optimization model is not complete, the model tends to incorporate such uncertainties as some assumptions on the coefficients. As time passes and more precise information is accumulated, the initial optimal solution may no longer be optimal, or even feasible. At this point, model builders want to modify the assumed and controllable coefficients to obtain the desired values of designated decision variables. To aid this process, a neural network could effectively be applied. So we develop a tool UNIK-OPT/NN which can support the construction and recall of the neural network model on top of the knowledge assisted optimization model formulator UNIK-OPT and the semantic neural network building aid UNIK-NEURO. By adopting a commonly interpretable semantic representation of optimization and neural network models, UNIK-OPT/NN can effectively automate most of the neural network construction and recall procedure for optimal control.

Original languageEnglish
Pages (from-to)43-62
Number of pages20
JournalDecision Support Systems
Volume18
Issue number1 SPEC. ISS.
DOIs
Publication statusPublished - 1996 Jan 1

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Neural Networks (Computer)
Semantics
Neural networks
Controllers
Uncertainty
Controller
Optimization model
Neural Networks
Neural Network Model

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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UNIK-OPT/NN neural network based adaptive optimal controller on optimization models. / Kim, Wooju; Lee, Jae K.

In: Decision Support Systems, Vol. 18, No. 1 SPEC. ISS., 01.01.1996, p. 43-62.

Research output: Contribution to journalArticle

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