Abstract
Fuzzy systems were shown to be universal approximators, so are their trainable variant, the neuro-fuzzy systems. But fuzzy systems suffer from the curse of dimensionality, i.e. a very strong increase in computational and memory demands with an increasing number of input variables. This paper describes the NetFAN-approach to reduce this drawback by decomposition. It also proofs that such decomposed systems are universal approximators. The benchmark example of modeling the energy and water consumption of a building not only demonstrates that it achieves approximation capabilities like artificial neural networks. It also gives a notion how to utilize abstract background knowledge.
Original language | English |
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Pages | 477-482 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 01.01.1997 |
Event | Proceedings of the 1997 6th IEEE International Conference on Fussy Systems - Barcelona, Spain Duration: 01.07.1997 → 05.07.1997 Conference number: 47111 |
Conference
Conference | Proceedings of the 1997 6th IEEE International Conference on Fussy Systems |
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Abbreviated title | FUZZ-IEEE'97 |
Country/Territory | Spain |
City | Barcelona |
Period | 01.07.97 → 05.07.97 |