NetFAN - A Structured Adaptive Fuzzy Approach

Olaf Huwendiek; Werner Brockmann

doi:10.1109/ICNN.1996.549048

NetFAN - A Structured Adaptive Fuzzy Approach

Olaf Huwendiek, Werner Brockmann

Institut für Technische Informatik

ZF Friedrichshafen AG

Abstract

Adaptive fuzzy systems are useful universal function approximators. But they suffer from the curse of dimensionality. I.e. the number of parameters, which have to be tuned, increases drastically if the number of input variables increases. This has the effect that memory and computational demands increase also drastically, and more stringently fitting problems may occur if the number of training data is limited. The approach presented in this paper addresses both problems by decomposing the functional mapping into a Network of Fuzzy Adaptive Nodes (NetFAN). This decomposition reduces the number of parameters as well as memory and computational demands. Some first investigations outline the basic characteristics of the NetFAN-approach.

Originalsprache	Englisch
Titel	Proceedings of International Conference on Neural Networks (ICNN'96)
Seitenumfang	6
Herausgeber (Verlag)	IEEE
Erscheinungsdatum	01.01.1996
Seiten	1079-1084
ISBN (Print)	0-7803-3210-5
DOIs	https://doi.org/10.1109/ICNN.1996.549048
Publikationsstatus	Veröffentlicht - 01.01.1996
Veranstaltung	Proceedings of the 1996 IEEE International Conference on Neural Networks - Washington, USA / Vereinigte Staaten Dauer: 03.06.1996 → 06.06.1996 Konferenznummer: 45420

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10.1109/ICNN.1996.549048

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title = "NetFAN - A Structured Adaptive Fuzzy Approach",

abstract = "Adaptive fuzzy systems are useful universal function approximators. But they suffer from the curse of dimensionality. I.e. the number of parameters, which have to be tuned, increases drastically if the number of input variables increases. This has the effect that memory and computational demands increase also drastically, and more stringently fitting problems may occur if the number of training data is limited. The approach presented in this paper addresses both problems by decomposing the functional mapping into a Network of Fuzzy Adaptive Nodes (NetFAN). This decomposition reduces the number of parameters as well as memory and computational demands. Some first investigations outline the basic characteristics of the NetFAN-approach.",

author = "Olaf Huwendiek and Werner Brockmann",

year = "1996",

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doi = "10.1109/ICNN.1996.549048",

language = "English",

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publisher = "IEEE",

pages = "1079--1084",

booktitle = "Proceedings of International Conference on Neural Networks (ICNN'96)",

note = "Proceedings of the 1996 IEEE International Conference on Neural Networks , ICNN 1996 ; Conference date: 03-06-1996 Through 06-06-1996",

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Huwendiek, O & Brockmann, W 1996, NetFAN - A Structured Adaptive Fuzzy Approach. in Proceedings of International Conference on Neural Networks (ICNN'96). IEEE International Conference on Neural Networks - Conference Proceedings, IEEE, S. 1079-1084, Proceedings of the 1996 IEEE International Conference on Neural Networks , Washington, Washington, USA / Vereinigte Staaten, 03.06.96. https://doi.org/10.1109/ICNN.1996.549048

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N2 - Adaptive fuzzy systems are useful universal function approximators. But they suffer from the curse of dimensionality. I.e. the number of parameters, which have to be tuned, increases drastically if the number of input variables increases. This has the effect that memory and computational demands increase also drastically, and more stringently fitting problems may occur if the number of training data is limited. The approach presented in this paper addresses both problems by decomposing the functional mapping into a Network of Fuzzy Adaptive Nodes (NetFAN). This decomposition reduces the number of parameters as well as memory and computational demands. Some first investigations outline the basic characteristics of the NetFAN-approach.

AB - Adaptive fuzzy systems are useful universal function approximators. But they suffer from the curse of dimensionality. I.e. the number of parameters, which have to be tuned, increases drastically if the number of input variables increases. This has the effect that memory and computational demands increase also drastically, and more stringently fitting problems may occur if the number of training data is limited. The approach presented in this paper addresses both problems by decomposing the functional mapping into a Network of Fuzzy Adaptive Nodes (NetFAN). This decomposition reduces the number of parameters as well as memory and computational demands. Some first investigations outline the basic characteristics of the NetFAN-approach.

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ER -

NetFAN - A Structured Adaptive Fuzzy Approach

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