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JRM Vol.32 No.4 pp. 745-752
doi: 10.20965/jrm.2020.p0745
(2020)

Paper:

Method to Expand the CMAC Model to Composite-Type Model

Jiro Morimoto*, Makoto Horio**, Yoshio Kaji*, Junji Kawata*, Mineo Higuchi*, and Shoichiro Fujisawa*

*Tokushima Bunri University
1314-1 Shido, Sanuki-si, Kagawa 769-2193, Japan

**Art Information Institute
1968 Hara, Mure, Takamatsu, Kagawa 761-0123, Japan

Received:
February 20, 2020
Accepted:
May 22, 2020
Published:
August 20, 2020
Keywords:
cerebellar model articulation controller (CMAC), classification problem, regression problem, learning algorithm
Abstract
Method to Expand the CMAC Model to Composite-Type Model

Structure of the expanded CMAC model

Neural networks (NNs) are effective for the learning of nonlinear systems, and thus they achieve satisfactory results in various fields. However, they require significant amount of training data and learning time. Notably, the cerebellar model articulation controller (CMAC), which is modeled after the cerebellar neural transmission system, proposed by Albus can effectively reduce learning time, compared with NNs. The CMAC model is often used to learn nonlinear systems that have continuously changing outputs, i.e., regression problems. However, the structure of the CMAC model must be expanded to apply it to classification problems as well. Additionally, the CMAC model finds it difficult to simultaneously classify categories and estimate their proportional linear measure because designated learning algorithms are required for both regression and classification problems. Therefore, we aim to build a composite-type CMAC model that combines classification and regression algorithms to simultaneously classify categories and estimate their proportional linear measures.

Cite this article as:
J. Morimoto, M. Horio, Y. Kaji, J. Kawata, M. Higuchi, and S. Fujisawa, “Method to Expand the CMAC Model to Composite-Type Model,” J. Robot. Mechatron., Vol.32, No.4, pp. 745-752, 2020.
Data files:
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Last updated on Dec. 03, 2020