Decision making with imprecise parameters

  • Authors:

  • Asli Celikyilmaz;I. Burhan Turksen

  • Affiliations:

  • Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA;Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ont., Canada and Department of Industrial Engineering, TOBB-Economy and Technology University, Sogutozu, Ankara ...

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2010

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Abstract

We analyze the impact of imprecise parameters on performance of an uncertainty-modeling tool presented in this paper. In particular, we present a reliable and efficient uncertainty-modeling tool, which enables dynamic capturing of interval-valued clusters representations sets and functions using well-known pattern recognition and machine learning algorithms. We mainly deal with imprecise learning parameters in identifying uncertainty intervals of membership value distributions and imprecise functions. In the experiments, we use the proposed system as a decision support tool for a production line process. Simulation results indicate that in comparison to benchmark methods such as well-known type-1 and type-2 system modeling tools, and statistical machine-learning algorithms, proposed interval-valued imprecise system modeling tool is more robust with less error.