Fuzzy diagnosis in AHU system using dynamic fuzzy neural network

Authors:
Du Juan;Er Meng Joo
Affiliations:
Computer Control Laboratory, School of Electric and Electronic Engineering, Nanyang Technological University, Republic of Singapore;Computer Control Laboratory, School of Electric and Electronic Engineering, Nanyang Technological University, Republic of Singapore
Venue:
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Year:
2003

Citing 2
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Abstract

In this paper, an efficient fault diagnosis method for air-handling unit using dynamic fuzzy neural networks (DFNNs) is presented. The proposed fault diagnosis method has the following salient features: (1) structure identification and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori; (2) fuzzy rules can be recruited or deleted dynamically; (3) fuzzy rules can be generated quickly without resorting to the backpropagation (BP) iteration learning, a common approach adopted by many existing methods. Simulation results demonstrate that high training and diagnosis speed and high diagnosis rate can be achieved.