Fuzzy inputs and missing data in similarity-based heterogeneous neural networks
Fuzzy heterogeneous networks are recently introduced 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...
| Main Authors: | , |
|---|---|
| Other Authors: | , |
| Format: | Conference Object |
| Language: | English |
| Published: |
Springer
1999
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/2117/184280 https://doi.org/10.1007/BFb0100554 |
| Summary: | Fuzzy heterogeneous networks are recently introduced 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 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 paper compares the effectiveness of the fuzzy heterogeneous model based on similarity with 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 performance, the effect of working with crisp or fuzzy imputs, the use of traditional scalar product vs. similarity-based functions, and the presence of missing data, are studied. The results obtained show that, from these standpoints, fuzzy heterogeneous networks based on similarity perform better than classical feed-forward models. This behaviour is consistent with previous results in other application domains. Peer Reviewed Postprint (author's final draft) |
|---|