Neural Computation, Vol 9, 403-417, Copyright © 1997 by The MIT Press
Adaptive Encoding Strongly Improves Function Approximation with CMAC
Martin Eldracher, Alexander Staller and René Pompl
The Cerebellar Model Arithmetic Computer (CMAC) (Albus 1981) is well known
as a good function approximator with local generalization abilities.
Depending on the smoothness of the function to be approximated, the
resolution as the smallest distinguishable part of the input domain plays a
crucial role. If the binary quantizing functions in CMAC are dropped in
favor of more general, continuous-valued functions, much better results in
function approximation for smooth functions are obtained in shorter
training time with less memory consumption. For functions with
discontinuities, we obtain a further improvement by adapting the continuous
encoding proposed in Eldracher and Geiger (1994) for
difficult-to-approximate areas. Based on the already far better function
approximation capability on continuous functions with a fixed topologically
distributed encoding scheme in CMAC (Eldracher et al.
1994), we present the better results in learning a two-valued function with
discontinuity using this adaptive topologically distributed encoding scheme
in CMAC.
