Abstract
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.
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Roadknight, C.M., Palmer-Brown, D., Mills, G.E. (1997). The analysis of artificial neural network data models. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052852
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DOI: https://doi.org/10.1007/BFb0052852
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