Prediction limit estimation for neural network models

Author

Chinman, R.B. ; Ding, J.

Author_Institution

Ind. & Manuf. Eng. Dept., North Dakota State Univ., Fargo, ND, USA

Volume

Issue

fYear

1998

fDate

11/1/1998 12:00:00 AM

Firstpage

1515

Lastpage

1522

Abstract

A novel method for estimation of prediction limits for global and local approximating neural networks is presented. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods, and calculates limits that indicate the extent to which one can rely on predictions for making future decisions

Keywords

approximation theory; modelling; self-organising feature maps; global approximating neural networks; input space partitioning; local approximating neural networks; local neighborhoods; neural network models; prediction limit estimation; self-organizing feature maps; Computer networks; Digital arithmetic; Feedforward neural networks; Feedforward systems; Function approximation; Lattices; Neural networks; Neurons; Predictive models; Testing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.728401

Filename

728401

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1442214