Multi-dimensional fuzzy interpolation neural network

Dayou Li; Yong Yue; Carsten Maple; Vitaly Schetinin; Hua Qiu

doi:10.1109/ICAL.2009.5262941

Multi-dimensional fuzzy interpolation neural network

Dayou Li^*, Yong Yue, Carsten Maple, Vitaly Schetinin, Hua Qiu

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

Abstract

This paper presents a multi-dimensional fuzzy interpolation neural network (FINN) which extends fuzzy interpolation that was developed to approximate single input single output functions to multi-dimensional space. The multidimensional fuzzy interpolation piecewise approximates multiple-input- single-output functions with small hyper-surfaces defined over fuzzy regions. The vertices of these fuzzy regions are represented by weighted multivariate fuzzy sets which are defined over the input space of a function. Optimally arranging the fuzzy sets in the input space can achieve arbitrary accurate approximations. The proposed FINN is able to establish the optimisation of the fuzzy sets. It was used to approximate the energy distribution of light for light chip and optical fibre alignment.

Original language	English
Title of host publication	Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009
Pages	186-190
Number of pages	5
DOIs	https://doi.org/10.1109/ICAL.2009.5262941
Publication status	Published - 2009
Externally published	Yes
Event	2009 IEEE International Conference on Automation and Logistics, ICAL 2009 - Shenyang, China Duration: 5 Aug 2009 → 7 Aug 2009

Publication series

Name	Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009

Conference

Conference	2009 IEEE International Conference on Automation and Logistics, ICAL 2009
Country/Territory	China
City	Shenyang
Period	5/08/09 → 7/08/09

Keywords

Fuzzy systems
Interpolation
Modeling

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10.1109/ICAL.2009.5262941

Cite this

Li, D., Yue, Y., Maple, C., Schetinin, V., & Qiu, H. (2009). Multi-dimensional fuzzy interpolation neural network. In Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009 (pp. 186-190). Article 5262941 (Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009). https://doi.org/10.1109/ICAL.2009.5262941

@inproceedings{8c92f0e9c7694f4a9e946f2b552e6443,

title = "Multi-dimensional fuzzy interpolation neural network",

abstract = "This paper presents a multi-dimensional fuzzy interpolation neural network (FINN) which extends fuzzy interpolation that was developed to approximate single input single output functions to multi-dimensional space. The multidimensional fuzzy interpolation piecewise approximates multiple-input- single-output functions with small hyper-surfaces defined over fuzzy regions. The vertices of these fuzzy regions are represented by weighted multivariate fuzzy sets which are defined over the input space of a function. Optimally arranging the fuzzy sets in the input space can achieve arbitrary accurate approximations. The proposed FINN is able to establish the optimisation of the fuzzy sets. It was used to approximate the energy distribution of light for light chip and optical fibre alignment.",

keywords = "Fuzzy systems, Interpolation, Modeling",

author = "Dayou Li and Yong Yue and Carsten Maple and Vitaly Schetinin and Hua Qiu",

year = "2009",

doi = "10.1109/ICAL.2009.5262941",

language = "English",

isbn = "9781424447954",

series = "Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009",

pages = "186--190",

booktitle = "Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009",

note = "2009 IEEE International Conference on Automation and Logistics, ICAL 2009 ; Conference date: 05-08-2009 Through 07-08-2009",

}

Li, D, Yue, Y, Maple, C, Schetinin, V & Qiu, H 2009, Multi-dimensional fuzzy interpolation neural network. in Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009., 5262941, Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009, pp. 186-190, 2009 IEEE International Conference on Automation and Logistics, ICAL 2009, Shenyang, China, 5/08/09. https://doi.org/10.1109/ICAL.2009.5262941

Multi-dimensional fuzzy interpolation neural network. / Li, Dayou; Yue, Yong; Maple, Carsten et al.
Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009. 2009. p. 186-190 5262941 (Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Multi-dimensional fuzzy interpolation neural network

AU - Li, Dayou

AU - Yue, Yong

AU - Maple, Carsten

AU - Schetinin, Vitaly

AU - Qiu, Hua

PY - 2009

Y1 - 2009

N2 - This paper presents a multi-dimensional fuzzy interpolation neural network (FINN) which extends fuzzy interpolation that was developed to approximate single input single output functions to multi-dimensional space. The multidimensional fuzzy interpolation piecewise approximates multiple-input- single-output functions with small hyper-surfaces defined over fuzzy regions. The vertices of these fuzzy regions are represented by weighted multivariate fuzzy sets which are defined over the input space of a function. Optimally arranging the fuzzy sets in the input space can achieve arbitrary accurate approximations. The proposed FINN is able to establish the optimisation of the fuzzy sets. It was used to approximate the energy distribution of light for light chip and optical fibre alignment.

AB - This paper presents a multi-dimensional fuzzy interpolation neural network (FINN) which extends fuzzy interpolation that was developed to approximate single input single output functions to multi-dimensional space. The multidimensional fuzzy interpolation piecewise approximates multiple-input- single-output functions with small hyper-surfaces defined over fuzzy regions. The vertices of these fuzzy regions are represented by weighted multivariate fuzzy sets which are defined over the input space of a function. Optimally arranging the fuzzy sets in the input space can achieve arbitrary accurate approximations. The proposed FINN is able to establish the optimisation of the fuzzy sets. It was used to approximate the energy distribution of light for light chip and optical fibre alignment.

KW - Fuzzy systems

KW - Interpolation

KW - Modeling

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U2 - 10.1109/ICAL.2009.5262941

DO - 10.1109/ICAL.2009.5262941

M3 - Conference Proceeding

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SN - 9781424447954

T3 - Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009

SP - 186

EP - 190

BT - Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009

T2 - 2009 IEEE International Conference on Automation and Logistics, ICAL 2009

Y2 - 5 August 2009 through 7 August 2009

ER -

Multi-dimensional fuzzy interpolation neural network

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Keywords

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