Adaptive networks for physical modeling

NEUROCOMPUTING

卷:20

Article

Szilas, N ; Cadoz, C

关键词: recurrent networks;    physical modeling;    active learning;   

DOI  :  10.1016/S0925-2312(98)00014-9

来源: Elsevier

【 摘 要 】

This paper presents an original link between neural networks theory and mechanical modeling networks. The problem is to find the parameters characterizing mechanical structures in order to reproduce given mechanical behaviors. Replacing neural units with mechanically based units and applying classical learning algorithms dedicated to supervised dynamic networks to these mechanical networks allows us to find the parameters for a physical model. Some new variants of real-time recurrent learning (RTRL) are also introduced, based on mechanical principles. The notion of interaction during learning is discussed at length and the results of tests are presented, Instead of the classical {machine learning system, environment} pair, we propose to study the {machine learning system, human operator, environment} triplet. Experiments have been carried out in simulated scenarios and some original experiments with a force-feedback device are also described. (C) 1998 Published by Elsevier Science B.V. All rights reserved.

【 授权许可】

Free