language: en

GROWING RADIAL BASIS FUNCTION NETWORKS

E. BLANZIERI *, P. KATENKAMP ** and A. GIORDANA ***
Centro di Scienza Cognitiva, Universita di Torino,
Via Lagrange 3, 10100 Torino, Italy.
e-mail: blanzier@psych.unito.it
flfl Institute for Real Time Systems and Robotics, University of Karlsruhe,
Germany.
flflfl Dipartimento di Informatica, Universita di Torino,
C.so Svizzera 185, 10149 Torino, Italy.
email: attilio@di.unito.it

Abstract. This paper presents and evaluates two algorithms for incrementally constructing Radial Basis Function Networks, a class of neural networks which looks more suitable for adtaptive control applications than the more popular backpropagation networks. The first algorithm has been derived by a previous method developed by Fritzke, while the second one has been inspired by the CART algorithm developed by Breiman for generation regression trees. Both algorithms proved to work well on a number of tests and exhibit comparable performances. An evaluation on the standard case study of the Mackey-Glass temporal series is reported.

Key Words. Machine Learning, Robotics, Neural Nets

1 INTRODUCTION