Fuzzy Diagnosis in AHU System Using Dynamic Fuzzy Neural Network

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.