Status已發表Published
Fault Diagnosis of Automotive Engines using Fuzzy Relevance Vector Machine
Wong, P. K.; Vong, C. M.; Zhang, Z.Y.; Xu, Q.S.
2011-09-01
Source PublicationTheoretical and Mathematical Foundations of Computer Science, Communications in Computer and Information Science
Publication PlaceGermany
PublisherSpringer-Verlag
Pages213-220
AbstractFor any faults of automotive engines, the diagnosis can be performed based on variety of symptoms. Traditionally, the description of the faulty symptom is just existence or not. However, this description cannot lead to a high accuracy because the symptom sometimes appears in different degrees. Therefore, a knowledge representation method which could precisely reflect the degree of the symptom is necessary. In this paper, the fuzzy logic is firstly applied to quantify the degrees of symptoms. A probabilistic classification system is then constructed by using the fuzzified symptoms and a new technique namely Fuzzy Relevance Vector Machine (FRVM). Moreover, both Fuzzy Probabilistic Neural Network (FPNN) and Fuzzy Probabilistic Support Vector Machine (FPSVM) are used to respectively construct similar classification systems for comparison with FRVM. Experimental results show that FRVM produces higher diagnosis performance than FPNN and FPSVM.
KeywordFuzzy Probabilistic Neural Network Fuzzy Probabilistic Support Vector Machine Fuzzy Relevance Vector Machine Engine fault diagnosis
Language英語English
ISBN9783642249983
The Source to ArticlePB_Publication
PUB ID9015
Document TypeBook chapter
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Recommended Citation
GB/T 7714
Wong, P. K.,Vong, C. M.,Zhang, Z.Y.,et al. Fault Diagnosis of Automotive Engines using Fuzzy Relevance Vector Machine[M]. Theoretical and Mathematical Foundations of Computer Science, Communications in Computer and Information Science, Germany:Springer-Verlag, 2011, 213-220.
APA Wong, P. K.., Vong, C. M.., Zhang, Z.Y.., & Xu, Q.S. (2011). Fault Diagnosis of Automotive Engines using Fuzzy Relevance Vector Machine. Theoretical and Mathematical Foundations of Computer Science, Communications in Computer and Information Science, 213-220.
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