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Fuzzy Dynamic Programming in the Stochastic Environment

  • Seiichi Iwamoto
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 73)

Abstract

From a viewpoint of stochastic dynamic programming, we develop “De -cision-making in a fuzzy environment” in Bellman and Zadeh’s seminal paper (1970). We present three dynamic programming approaches (1) membership-parametric method, (2) history-parametric method and (3) multi-stage stochastic decision tree-table method —, which yield a common optimal solution. We also introduce a dynamic programming approach to both fuzzy system and an a posteriori conditional decision process. We show that their stochastic decision process is the a posteriori conditional decision process.

Keywords

Fuzzy System Optimal Policy Stochastic System Recursive Formula General Policy 
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 2001

Authors and Affiliations

  • Seiichi Iwamoto
    • 1
  1. 1.Department of Economic EngineeringGraduate School of EconomicsHigashiku, FukuokaJapan

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