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Paper   IPM / Cognitive / 7525
School of Cognitive Sciences
  Title:   Implementation of A Genetically Optimized Interpolative Fuzzy Inference Engine In Controlling A Ball-Plate Laboratory Setup
1.  C. Lucas
2.  A. Fatehi
3.  d. Shahmirzadi
4.  M. Takami
  Status:   In Proceedings
  Proceeding: Engineering Technical Conference and Computers and Information in Engineering Conference Chicago
  Year:  2003
  Supported by:  IPM
This paper goes through giving the results of implementing a genetically optimized interpolative fuzzy engine in controlling a ball-plate laboratory setup. For demonstrating the advantages of our proposed interpolative fuzzy controller over classical fuzzy controllers, we gave some comparison between the fuzzy controllers, one with common CRI inference mechanism and one with our interpolative inference mechanism. As expected, our fuzzy interpolative controller is more efficient than CRI-based controller, with respect to computational space, as well as it is more robust to goal achievement.

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