Paper Info
Title
Robust and fast learning for fuzzy cerebellar model articulation controllers.
Abstract
In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embeding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training data. An annealing schedule is also adopted for the learning constant to fulfill robust learning. In the study, we also extend our previous work of adopting the credit assignment idea into CMAC learning to provide fast learning for fuzzy CMAC. From demonstrated examples, it is clearly evident that the proposed algorithm indeed has faster and more robust learning. In our study, we then employ the proposed CMAC for an online learning control scheme used in the literature. In the implementation, we also propose to use a tuning parameter instead of a fixed constant to achieve both online learning and fine-tuning effects. The simulation results indeed show the effectiveness of the proposed approaches.
Year
DOI
Venue
2006
10.1109/TSMCB.2005.855570
IEEE Transactions on Systems, Man, and Cybernetics, Part B
Keywords
Field
DocType
credit assignment idea,robust property,online learning,annealing schedule,traditional cmac,proposed algorithm,robust learning,fuzzy cmac,fuzzy cerebellar model articulation,proposed cmac,learning artificial intelligence,robust control,fuzzy control,simulated annealing,cmac
Simulated annealing,Semi-supervised learning,Embedding,Active learning (machine learning),Computer science,Fuzzy logic,Outlier,Artificial intelligence,Fuzzy control system,Robust control,Machine learning
Journal
Volume
Issue
ISSN
36
1
1083-4419
Citations 
PageRank 
References 
29
1.11
20
Authors
3
Name
Order
Citations
PageRank
Shun-Feng Su1119497.62
Zne-Jung Lee294043.45
Yan-Ping Wang3291.11