Title :
CMAC neural networks structures
Author :
Mohajeri, Kamran ; Pishehvar, Ghasem ; Seifi, Mohammad
Author_Institution :
North Power Transm. Maintenance Co., Sari, Iran
Abstract :
Cerebellar Model Articulation Controller (CMAC) NN is a computational model of cerebellum introduced as an alternative to backpropagated multilayer networks to control robot arms. From then it has seen many improvements and has been applied in many other areas as a general NN. These improvements have been in the context of generalization, learning techniques, differentiability, memory size, fuzzification and hardware implementation. This paper is a systematic review of CMAC´s different structures and applications.
Keywords :
fuzzy set theory; manipulators; neural nets; CMAC neural networks structures; cerebellar model articulation controller; computational model; control robot arms; differentiability; fuzzification; hardware implementation; learning techniques; memory size; multilayer networks; Brain modeling; Computer networks; Function approximation; Hardware; Hypercubes; Least squares approximation; Multi-layer neural network; Multilayer perceptrons; Neural networks; Robot control;
Conference_Titel :
Computational Intelligence in Robotics and Automation (CIRA), 2009 IEEE International Symposium on
Conference_Location :
Daejeon
Print_ISBN :
978-1-4244-4808-1
Electronic_ISBN :
978-1-4244-4809-8
DOI :
10.1109/CIRA.2009.5423175
