Contents
A novel method of type 2 fuzzy logic inference is presented in this chapter. The method is highly efficient regarding computational time and implementation effort. Type-2 input membership functions are optimized using the Human Evolutionary Model (HEM) considering as the objective function the Integral of Squared Error at the controllers output. Statistical tests were achieved considering how the error at the controller’s output is diminished in presence of uncertainty, demonstrating that the proposed method outperforms an optimized traditional type-2 fuzzy controller for the same test conditions.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Keywords
- Fuzzy Logic Controller
- Fuzzy Logic System
- Membership Function
- Fuzzy Logic Inference
- Parameter Tuning Algorithm
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.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Castillo, O., Melin, P. (2007). 4 A Method for Type-2 Fuzzy Inference in Control Applications. In: Type-2 Fuzzy Logic: Theory and Applications. Studies in Fuzziness and Soft Computing, vol 223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76284-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-76284-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76283-6
Online ISBN: 978-3-540-76284-3
eBook Packages: EngineeringEngineering (R0)