Fuzzy Inputs and Missing Data in Similarity-Based Heterogeneous Neural Networks
Fuzzy heterogeneous networks are recently introduced feed-forward neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with discrete quantities, also admitting missing data. These networks have net input funct...
| Main Authors: | , |
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| Format: | Text |
| Language: | English |
| Published: |
1999
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.8090 http://goliat.upc.es/dept/techreps/ps/R98-66.ps.gz |
| Summary: | Fuzzy heterogeneous networks are recently introduced feed-forward neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with discrete quantities, also admitting missing data. These networks have net input functions based on similarity relations between the inputs to and the weights of a neuron. They thus accept heterogeneous --possibly missing-- inputs, and can be coupled with classical neurons in hybrid network architectures, trained by means of genetic algorithms or other evolutionary methods. This report compares the effectiveness of the fuzzy heterogeneous model based on similarity with that of the classical feed-forward one, in the context of an investigation in the field of environmental sciences, namely, the geochemical study of natural waters in the Arctic (Spitzbergen). Classification accuracy, the effect of working with crisp or fuzzy inputs, the use of traditional scalar product vs. si. |
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