language: en

[Proceedings of the 4th International Symposium on Intelligent Robotic Systems SIRS'96, Lisbon, Portugal, July 22-26, 1996]

Memory-based Stochastic Optimization for Automated Tuning of Neural Network's High Level Parameters ?

Artur Dubrawski **
The Robotics Institute, Carnegie Mellon University,
5000 Forbes Avenue, Pittsburgh, PA 15213, USA

Abstract. In this paper we describe a new method for automated tuning of high level parameters of supervised learning systems. It uses memory-based learning principles and follows certain ideas of experimental design. The described method allows not only for an efficient search through a decision space, but also for a concurrent validation of the learning algorithm performance on a given data. Potential usefulness of the proposed approach is illustrated with the Fuzzy-ARTMAP neural network application to learning a qualitative positioning of an indoor mobile robot equipped with sonar range sensors. Automatically selected neural network setpoints reach a comparable performance to those achieved by human experts in relatively simple 2D cases. Migration of the proposed method to higher order optimization domains bears a big promise, but requires further research.

1 Introduction