Intuitionistic Fuzzy Neural Networks based on Extended Kalman Filter Training algorithm

DOI

10.2991/ccis-13.2013.76How to use a DOI?

Keywords

Intuitionistic fuzzy neural networks;training algorithm;Extended Kalman Filter

Abstract

In this paper, an intuitionistic fuzzy neural network model is proposed The network structure has five layers, and adopts Mandani’s fuzzy reasoning. A new fuzzy inference system is applied in the model, which contains hesitation margin as a part. A training algorithm based on Extended Klaman Filter(EKF) is development. The EKF procedure to update parameter is introduced. The derivation of EKF based on adaptation algorithm for intuitionistic fuzzy adaptive equalizer is given. An example is given to demonstrate the intuitionistic fuzzy neural network based on EKF training algorithm has a good function approximate performance.

Copyright

© 2013, 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  - Zhou Xiaoguang
AU  - Zhao Renhou
AU  - Shang Xiumin
AU  - Zhang Lili
PY  - 2013/11
DA  - 2013/11
TI  - Intuitionistic Fuzzy Neural Networks based on Extended Kalman Filter Training algorithm
BT  - Proceedings of the The 1st International Workshop on Cloud Computing and Information Security
PB  - Atlantis Press
SP  - 328
EP  - 331
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccis-13.2013.76
DO  - 10.2991/ccis-13.2013.76
ID  - Xiaoguang2013/11
ER  -