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Adaptive Database Learning in Decision Support Systems Using Evolutionary Fuzzy Systems: A Generic Framework

  • Cong Tran
  • Lakhmi C. Jain
  • Ajith Abraham
Part of the Advances in Soft Computing book series (AINSC, volume 14)

Abstract

Normally a decision support system is build to solve problem where multi-criteria decisions are involved. The database is the vital part of the decision support containing the information or data that is used in decision-making process. This is the field where engineers and scientists try to apply several heuristics and soft computing techniques such as learning, search and modelling the imprecise information to obtain optimal decisions. In this paper, we present the proposed a generic framework using evolutionary fuzzy systems to obtain decision rules automatically and the usage of fuzzy inference system to process the imprecise information. Some simulation results demonstrating the difficulties to decide the optimal quantity of membership functions, shape and parameters are also provided.

Keywords

Membership Function Decision Support System Fuzzy Rule Fuzzy Inference System Membership Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Cong Tran
    • 1
  • Lakhmi C. Jain
    • 1
  • Ajith Abraham
    • 2
  1. 1.School of Electrical and Information EngineeringUniversity of South AustraliaAdelaideAustralia
  2. 2.School of computing and Information TechnologyMonash University (Gippsland campus)Australia

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