DocumentCode
1625223
Title
Fuzzy CMAC structures
Author
Mohajeri, Kamran ; Zakizadeh, Manijeh ; Moaveni, Bijan ; Teshnehlab, Mohammad
Author_Institution
North power Transm. Maintenance Co.y, Sari, Iran
fYear
2009
Firstpage
2126
Lastpage
2131
Abstract
Cerebellum model articulation controller (CMAC) is known as a feedforward neural network (NN) with fast learning and performance. Many improvements have been introduced to it which fuzzy CMAC (FCMAC) is the most important one. Fuzzy CMAC as a neuro fuzzy system increases precision, reduces memory size and makes CMAC differentiable. In addition FCMAC converts CMAC NN as a black box to a white box that its operation is interpretable using fuzzy rules. Fuzzy CMAC has not a unique structure in literature and there are differences in many aspects as membership function, memory layered structure, deffuzification and the fuzzy system applied. Discussing these, this paper reviews fuzzy CMAC different structures in literature.
Keywords
cerebellar model arithmetic computers; feedforward neural nets; fuzzy neural nets; cerebellum model articulation controller; feedforward neural network; fuzzy CMAC; fuzzy rule; membership function; memory layered structure; neuro fuzzy system; Brain modeling; Computational modeling; Control systems; Feedforward neural networks; Fuzzy systems; Neural networks; Power system modeling; Power transmission; Quantization; Robot control;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location
Jeju Island
ISSN
1098-7584
Print_ISBN
978-1-4244-3596-8
Electronic_ISBN
1098-7584
Type
conf
DOI
10.1109/FUZZY.2009.5277185
Filename
5277185
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