Simulated Annealing for Automated Definition of Fuzzy Sets in Human Central Nervous System Modeling

  • Francisco Mugica
  • Angela Nebot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

The main goal of this research is to study the usefulness of the Simulated Annealing (SA) approach, developed in the context of the Fuzzy Inductive Reasoning (FIR) methodology, for the automatic identification of fuzzy partitions in the human Central Nervous System (CNS) modeling problem. The SA algorithm can be viewed as a pre-process of the FIR methodology that allows the modeler to use it in a more efficient way. Two different SA algorithm cost functions have been studied and evaluated in this paper. The new approach is applied to obtain accurate models for the five controllers that compose the CNS. The results are compared and discussed with those obtained by other inductive methodologies for the same problem.

Keywords

Cost Function Mean Square Error Simulated Annealing Simulated Annealing Algorithm Fuzzy Partition 
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 2003

Authors and Affiliations

  • Francisco Mugica
    • 1
  • Angela Nebot
    • 2
  1. 1.Instituto Latinoamericano de la Comunicación EducativaMéxico D.F.México
  2. 2.Dept. Llenguatges i Sistemes InformàticsUniversitat Politècnica de CatalunyaBarcelonaSpain

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