The Kernel Recursive Least Squares CMAC with Vector Eligibility

作者：Laufer Carl^*; Patel Nitish; Coghill George

来源：Neural Processing Letters, 2014, 39(3): 269-284.

DOI：10.1007/s11063-013-9303-z

• Cerebellar model articulation controller (CMAC)
• Kernel recursive least squares (KRLS)
• Vector eligibility
• FOX-CMAC

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.

• 出版日期2014-6