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IMA Journal of Mathematical Control and Information 1988 5(4):315-331; doi:10.1093/imamci/5.4.315
© 1988 by Institute of Mathematics and its Applications
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On the convergence of the Albus Perceptron

D. ELLISON

Dundee College of Technology Bell Street, Dundee, DD1 1HG

Albus (1975a,b, 1981) developed CMAC, an adaptive system for robotic control, based on the cerebellum and the classical perceptron. He applied it to controlling a physical model of the human arm, with seven degrees of freedom. The system exhibited the classical learning curve, generalization, and learning interference. This paper proves the convergence of this learning scheme.

Computer experiments are presented investigating the effects of training strategy and generalization width on the rate of convergence and on learning interference.


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