Page 107 - Rapid Learning in Robotics

6.7 Summary 93

aim at improving the mapping accuracy and the computational efficiency
with larger training sets.
The proposed “Local-PSOM” algorithm constructs the constant sized
PSOM on a dynamically determined sub-grid and keeps the computa-
tional effort constant when the number of training points increases. Our
results suggest an excellent cost–benefit relation in cases with more than
four nodes per axes.
An alternative to improve the mapping accuracy is the use of the “Cheby-
shev spaced PSOM” exploiting the superior approximation capabilities of
the Chebyshev polynomials for the design of the internal basis functions.
This imposes no extra effort but offers a significant precision advantage
when using four or more nodes per axes.