Input projection method for safe use of neural networks based on process data

Author

Hämäläinen, Jari J. ; Järvimäki, IIpo

Author_Institution

Tech. Res. Centre of Finland, VTT Autom., Finland

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

193

Abstract

A principle is introduced for avoiding the use of a neural network model outside its validity domain defined by observations used for training and validation. The principle is based on clustering the regressor training data and projecting the input vector to the validity domain described by the cluster centers. The confidence level for the projection can be specified. The method is illustrated by two examples: a friction force model in an elevator simulator and a model of lime re-burning in a rotary kiln

Keywords

chemical industry; feedforward neural nets; lifts; neurocontrollers; process control; clustering; confidence level; elevator simulator; feedforward neural networks; friction force model; input projection method; lime reburning model; process control; regressor training data; rotary kiln; validity domain; Automation; Electronic mail; Elevators; Feedforward neural networks; Friction; Kilns; Neural networks; Predictive models; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.682261

Filename

682261