The Kernel Recursive Least Squares CMAC with Vector Eligibility
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- 作者：Carl Laufer (1)
Nitish Patel (1)
George Coghill (1) - 关键词：Cerebellar model articulation controller (CMAC) ; Kernel recursive least squares (KRLS) ; Vector eligibility ; FOX–CMAC
- 刊名：Neural Processing Letters
- 出版年：2014
- 出版时间：June 2014
- 年：2014
- 卷：39
- 期：3
- 页码：269-284
- 全文大小：
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Nitish Patel (1)
George Coghill (1)
1. Department of Electrical and Computer Engineering, University of Auckland, Electrical and Computer Engineering Building 303, Room 240 Level 2, Science Centre 38 Princes Street, Auckland, New Zealand - ISSN：1573-773X
文摘
The cerebellar model articulation controller (CMAC) neural network is an associative memory that is biologically inspired by the cerebellum, which is found in the brains of animals. In recent works, the kernel recursive least squares CMAC (KRLS–CMAC) was proposed as a superior alternative to the standard CMAC as it converges faster and does not require tuning of a learning rate parameter. One improvement to the standard CMAC that has been discussed in the literature is eligibility, and vector eligibility. With vector eligibility the CMAC is able to control online motion control problems that it could not previously, stabilize the system much faster, and converge to a more intelligent solution. This paper integrates vector eligibility with the KRLS–CMAC and shows how the combination is advantageous through two simulated control experiments.