Using Genetic-Fuzzy-Neuro Model to Design Dual-FNNs Controller

DOI

10.2991/iske.2007.269How to use a DOI?

Keywords

Dual-FNN, Genetic algorithms, Fuzzy logic, Neural networks, G-F-N model

Abstract

During the last decade, there has been increased use of neural networks (NNs), fuzzy logic (FL) and genetic algorithms (GAs) in artificial intelligence (AI). Since these three methods are complementary rather than competitive, a better performance model which has combined GAs, FL and NNs comes into being gradually. This paper presents genetic-fuzzy-neuro (G-F-N) model to design the dual-fuzzy neural-networks (DFNNs) controller. For the convenience of adaptive control, the structure of the two-fuzzy neural-network controller is divided into two parts. Each part is a fuzzy neural-network (FNN). The adaptive controller uses two FNNs. One FNN is used to identify a fuzzy model of controlled object. The other is online-tracking learning the suitable control policy

Copyright

© 2007, 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  - Kaijun Xu
AU  - Jiajun Lai
AU  - Shuiting Wu
AU  - Yang Xu
PY  - 2007/10
DA  - 2007/10
TI  - Using Genetic-Fuzzy-Neuro Model to Design Dual-FNNs Controller
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1575
EP  - 1580
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.269
DO  - 10.2991/iske.2007.269
ID  - Xu2007/10
ER  -