Title :
Fault Diagnosis of Gas Blower Based on Genetic Fuzzy Neural Network
Author :
Fangxia, Hu ; Jie, Liu ; Xinglong, Chen
Author_Institution :
Dept. of Eng. Technol., Chongqing Technol. & Bus. Inst., Chongqing, China
Abstract :
In order to make full use of the capability of GA´s global searching and BP network´s local searching, a genetic fuzzy neural network model is proposed. And the way of fault characteristic parameters´ fuzzy processing and optimizing the weights and thresholds of ANN by GA are studied. As a result, the convergence speed and convergence precision are greatly increased. Application to the fault diagnosis of a gas blower system shows that the new model overcomes the low learning rate and local minimum of BP algorithm and the fault diagnosis precision is effectively improved.
Keywords :
backpropagation; fault diagnosis; fuzzy neural nets; genetic algorithms; machinery; backpropagation; convergence precision; convergence speed; fault diagnosis; gas blower; genetic fuzzy neural network; optimization; Artificial neural networks; Convergence; Educational institutions; Fault diagnosis; Fuzzy logic; Fuzzy neural networks; Genetic algorithms; Neural networks; Robustness; Software engineering; fault diagnosis; fuzzy processing; gas blower; genetic algorithm; neural network;
Conference_Titel :
Software Engineering, 2009. WCSE '09. WRI World Congress on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3570-8
DOI :
10.1109/WCSE.2009.217