Enhanced Fuzzy Systems for Type 2 Fuzzy and their Application in Dynamic System Identification

DOI

10.2991/ifsa-eusflat-15.2015.20How to use a DOI?

Keywords

Enhanced fuzzy systems, type-2 fuzzy set, back propagation, neural network systems.

Abstract

The paper proposes a novel fuzzy system structure to enhance the performance of fuzzy neural network systems. The structure of enhanced fuzzy system (EFS) is to decompose each fuzzy variable into fuzzy subsystems called component fuzzy systems to act as type 2 fuzzy, and each component fuzzy system is based on one traditional fuzzy set with one pair of symmetry fuzzy sets. In addition, in order to illustrate the performance of EFS, the paper utilizes the common back propagation learning algorithm for neural networks in the identification of dynamic systems. From simulation results, it is evident that the proposed EFS have much faster convergent speed in terms of epochs in the tracking model and better testing error than those of using other identification methods.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shun-Feng Su
AU  - Ming-Chang Chen
PY  - 2015/06
DA  - 2015/06
TI  - Enhanced Fuzzy Systems for Type 2 Fuzzy and their Application in Dynamic System Identification
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 118
EP  - 122
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.20
DO  - 10.2991/ifsa-eusflat-15.2015.20
ID  - Su2015/06
ER  -