The analysis of artificial neural network data models

C. M. Roadknight, D. Palmer-Brown, G. E. Mills

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Artificial neural networks are good non-linear function approximators but their multi-layer, non-linear form gives little immediate indication of the features they have learnt. Several methods are put forward in this paper that reduce the complexity of the network or give simplified equations that are easier to interpret. Relative weight analysis and equation synthesis are summarised while correlated activity pruning is introduced and explained in detail. The former techniques use the weights of a trained network to assign importance to inputs or groups of inputs. The latter algorithm reduces complexity of a network by merging hidden units that have correlated activations. This procedure also allows the relationship between detected features to be evaluated. Data from pollutant impact studies are used but the techniques developed are applicable to many scientific data modelling environments.

Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis
Subtitle of host publicationReasoning about Data - 2nd International Symposium, IDA-1997, Proceedings
EditorsXiaohui Liu, Paul Cohen, Michael Berthold
PublisherSpringer Nature
Number of pages10
ISBN (Print)9783540633464
Publication statusPublished - 1997
Event2nd International Symposium on Intelligent Data Analysis, IDA 1997 - London, United Kingdom
Duration: 4 Aug 19976 Aug 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Symposium on Intelligent Data Analysis, IDA 1997
Country/TerritoryUnited Kingdom


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