Learning efficiency improvement of fuzzy CMAC by aitken acceleration method

Chin Ming Hong; Chih Ming Chen; Hung Yu Chien

Learning efficiency improvement of fuzzy CMAC by aitken acceleration method

Chin Ming Hong, Chih Ming Chen, Hung Yu Chien

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Fuzzy CMAC (FCMAC) was developed by Ker in 1997 [4] and it has advantages in terms of simple learning structure, fast convergence speed, easy implementation by hardware and the preservation of derivative information. It has been successfully applied to control field. To enhance on-line learning ability of FCMAC [7], this study presents a learning efficiency improvement approach based on Aitken acceleration method for fuzzy CMAC (FCMAC). This approach employs the forecast ability of Aitken acceleration method to shorten the training time as well as promote the learning accuracy of FCMAC. The experimental results show that the proposed Aitken acceleration method not only help FCMAC to promote the learning convergence speed, but also has more accurate learning ability than the original FCMAC.

Original language	English
Title of host publication	17th International Conference on Software Engineering and Knowledge Engineering, SEKE 2005
Pages	556-559
Number of pages	4
Publication status	Published - 2005
Event	17th International Conference on Software Engineering and Knowledge Engineering, SEKE 2005 - Taipei, Taiwan Duration: 2005 Jul 14 → 2005 Jul 16

Other

Other	17th International Conference on Software Engineering and Knowledge Engineering, SEKE 2005
Country	Taiwan
City	Taipei
Period	05/7/14 → 05/7/16

Keywords

Aitken acceleration
Cerebellar model articulation controller
Fuzzy CMAC
Fuzzy theory
Prediction

ASJC Scopus subject areas

Software

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title = "Learning efficiency improvement of fuzzy CMAC by aitken acceleration method",

abstract = "Fuzzy CMAC (FCMAC) was developed by Ker in 1997 [4] and it has advantages in terms of simple learning structure, fast convergence speed, easy implementation by hardware and the preservation of derivative information. It has been successfully applied to control field. To enhance on-line learning ability of FCMAC [7], this study presents a learning efficiency improvement approach based on Aitken acceleration method for fuzzy CMAC (FCMAC). This approach employs the forecast ability of Aitken acceleration method to shorten the training time as well as promote the learning accuracy of FCMAC. The experimental results show that the proposed Aitken acceleration method not only help FCMAC to promote the learning convergence speed, but also has more accurate learning ability than the original FCMAC.",

keywords = "Aitken acceleration, Cerebellar model articulation controller, Fuzzy CMAC, Fuzzy theory, Prediction",

author = "Hong, {Chin Ming} and Chen, {Chih Ming} and Chien, {Hung Yu}",

year = "2005",

language = "English",

isbn = "9781627486590",

pages = "556--559",

booktitle = "17th International Conference on Software Engineering and Knowledge Engineering, SEKE 2005",

note = "17th International Conference on Software Engineering and Knowledge Engineering, SEKE 2005 ; Conference date: 14-07-2005 Through 16-07-2005",

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TY - GEN

T1 - Learning efficiency improvement of fuzzy CMAC by aitken acceleration method

AU - Hong, Chin Ming

AU - Chen, Chih Ming

AU - Chien, Hung Yu

PY - 2005

Y1 - 2005

N2 - Fuzzy CMAC (FCMAC) was developed by Ker in 1997 [4] and it has advantages in terms of simple learning structure, fast convergence speed, easy implementation by hardware and the preservation of derivative information. It has been successfully applied to control field. To enhance on-line learning ability of FCMAC [7], this study presents a learning efficiency improvement approach based on Aitken acceleration method for fuzzy CMAC (FCMAC). This approach employs the forecast ability of Aitken acceleration method to shorten the training time as well as promote the learning accuracy of FCMAC. The experimental results show that the proposed Aitken acceleration method not only help FCMAC to promote the learning convergence speed, but also has more accurate learning ability than the original FCMAC.

AB - Fuzzy CMAC (FCMAC) was developed by Ker in 1997 [4] and it has advantages in terms of simple learning structure, fast convergence speed, easy implementation by hardware and the preservation of derivative information. It has been successfully applied to control field. To enhance on-line learning ability of FCMAC [7], this study presents a learning efficiency improvement approach based on Aitken acceleration method for fuzzy CMAC (FCMAC). This approach employs the forecast ability of Aitken acceleration method to shorten the training time as well as promote the learning accuracy of FCMAC. The experimental results show that the proposed Aitken acceleration method not only help FCMAC to promote the learning convergence speed, but also has more accurate learning ability than the original FCMAC.

KW - Aitken acceleration

KW - Cerebellar model articulation controller

KW - Fuzzy CMAC

KW - Fuzzy theory

KW - Prediction

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Y2 - 14 July 2005 through 16 July 2005

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