The cerebellar model articulation controller, or CMAC, is a type of associative memory neural network suitable for use in direct adaptive control schemes. However, the CMAC exhibits a large trade-off between stability and performance when inputs oscillate. This is due to the local nature of the basis functions—an input oscillating between two basis functions on one layer can cause their weights to drift in opposite directions. Continued drift will eventually affect performance, resulting in bursting. The proposed method overlaps the basis functions on each layer so that an oscillation will occur within basis functions. This makes the weights much less prone to drift. A simulation with a flexible joint demonstrates that both high performance and stability can be achieved using the proposed method.
Soft Computing – Springer Journals
Published: May 27, 2016
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