Title :
An Improved Fuzzy Neural Network and Its Application in Machine Fault Diagnosis
Author :
Linfeng Deng ; Rongzhen Zhao
Author_Institution :
Coll. of Mech. & Electron. Eng., Lanzhou Univ. of Technol., Lanzhou, China
Abstract :
This paper presents an improved fuzzy neural network (IFNN) for pattern recognition. The IFNN consists of several sub-networks, which represent different patterns. Each sub-network distinguishes a particular pattern from others, and each pattern corresponds to the certain inputs. In IFNN, an empirical formula tested many times is used to calculate the number of nodes in the hidden layer, and the learning algorithm with 3 self-adjustable coefficients is utilized to improve the learning efficiency of the training process. After that the ultimate outputs represent the degree of the state data belonging to the specific pattern. The performance of the IFNN was tested and verified by an example of machine fault diagnosis, and another issue about knowledge discovery was put forward.
Keywords :
data mining; fault diagnosis; fuzzy neural nets; learning (artificial intelligence); mechanical engineering computing; pattern recognition; improved fuzzy neural network; knowledge discovery; learning algorithm; machine fault diagnosis; pattern recognition; self-adjustable coefficients; training process; Artificial neural networks; Fault diagnosis; Fuzzy control; Fuzzy neural networks; Pattern recognition; Testing; Training; IFNN; machine fault diagnosis; pattern recognition; sub-network;
Conference_Titel :
Computational and Information Sciences (ICCIS), 2010 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8814-8
Electronic_ISBN :
978-0-7695-4270-6
DOI :
10.1109/ICCIS.2010.59
