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Interval Type 2 Neuro-Fuzzy Systems Based on Interval Consequents

  • Janusz Starczewski
  • Leszek Rutkowski
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Abstract

There are several ways to synthesize fuzzy systems and neural networks. The so-called neuro-fuzzy systems exhibit advantages of both techniques, namely learning abilities of neural networks and natural language description of fuzzy systems. Recently the concept of type 2 fuzzy sets, i.e. fuzzy sets with fuzzy membership grades, was introduced to fuzzy inference systems. This paper presents a new neuro-fuzzy system of type 2 derived under the assumption that the rule antecedents are characterized by interval fuzzy membership grades and the consequents are intervals. An application for the checking of the driver’s steering behaviors is given as an example.

Keywords

Root Mean Square Error Fuzzy System Fuzzy Inference System Interval Type Natural Language Description 
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

  • Janusz Starczewski
    • 1
  • Leszek Rutkowski
    • 1
  1. 1.Department of Computer EngineeringCzęstochowa University of TechnologyCzęstochowaPoland

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