Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System

Chia Hao Tu; Chunshien Li

doi:10.1007/978-3-319-75417-8_23

Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System

Chia Hao Tu
, Chunshien Li^*

^*Corresponding author for this work

National Central University

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

11 Scopus citations

Abstract

A complex-fuzzy machine learning approach to function approximation for multiple functions is proposed in this paper. The proposed approach involves the utility of complex-valued vector outputs by a novel complex-fuzzy model using complex fuzzy sets and the famous PSO-RLSE hybrid algorithm for machine learning of the model. An experiment was used to test the proposed approach for the ability of approximating four functions simultaneously. With the experimental result, the performance by the proposed model is promising and the proposed approach is compared to other methods. With complex fuzzy sets, the proposed approach has shown the excellent capability of function approximation for multiple functions using one single model with good performance.

Original language	English
Title of host publication	Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings
Editors	Hoang Pham, Ngoc Thanh Nguyen, Bogdan Trawinski, Duong Hung Hoang, Tzung-Pei Hong
Publisher	Springer Verlag
Pages	243-254
Number of pages	12
ISBN (Print)	9783319754161
DOIs	https://doi.org/10.1007/978-3-319-75417-8_23
State	Published - 2018
Externally published	Yes
Event	10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018 - Dong Hoi City, Viet Nam Duration: 19 03 2018 → 21 03 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10751 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018
Country/Territory	Viet Nam
City	Dong Hoi City
Period	19/03/18 → 21/03/18

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

Keywords

Complex fuzzy set
Function approximation
Fuzzy system
Multi-target prediction

Access to Document

10.1007/978-3-319-75417-8_23

Cite this

Tu, C. H., & Li, C. (2018). Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System. In H. Pham, N. T. Nguyen, B. Trawinski, D. H. Hoang, & T.-P. Hong (Eds.), Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings (pp. 243-254). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10751 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-75417-8_23

Tu, Chia Hao ; Li, Chunshien. / Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System. Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings. editor / Hoang Pham ; Ngoc Thanh Nguyen ; Bogdan Trawinski ; Duong Hung Hoang ; Tzung-Pei Hong. Springer Verlag, 2018. pp. 243-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{71782a0b63bd4ac9ba87a5bcc45b0e6b,

title = "Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System",

abstract = "A complex-fuzzy machine learning approach to function approximation for multiple functions is proposed in this paper. The proposed approach involves the utility of complex-valued vector outputs by a novel complex-fuzzy model using complex fuzzy sets and the famous PSO-RLSE hybrid algorithm for machine learning of the model. An experiment was used to test the proposed approach for the ability of approximating four functions simultaneously. With the experimental result, the performance by the proposed model is promising and the proposed approach is compared to other methods. With complex fuzzy sets, the proposed approach has shown the excellent capability of function approximation for multiple functions using one single model with good performance.",

keywords = "Complex fuzzy set, Function approximation, Fuzzy system, Multi-target prediction",

author = "Tu, \{Chia Hao\} and Chunshien Li",

note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018 ; Conference date: 19-03-2018 Through 21-03-2018",

year = "2018",

doi = "10.1007/978-3-319-75417-8\_23",

language = "英语",

isbn = "9783319754161",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "243--254",

editor = "Hoang Pham and Nguyen, \{Ngoc Thanh\} and Bogdan Trawinski and Hoang, \{Duong Hung\} and Tzung-Pei Hong",

booktitle = "Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings",

address = "德国",

}

Tu, CH & Li, C 2018, Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System. in H Pham, NT Nguyen, B Trawinski, DH Hoang & T-P Hong (eds), Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10751 LNAI, Springer Verlag, pp. 243-254, 10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018, Dong Hoi City, Viet Nam, 19/03/18. https://doi.org/10.1007/978-3-319-75417-8_23

Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System. / Tu, Chia Hao; Li, Chunshien.
Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings. ed. / Hoang Pham; Ngoc Thanh Nguyen; Bogdan Trawinski; Duong Hung Hoang; Tzung-Pei Hong. Springer Verlag, 2018. p. 243-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10751 LNAI).

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

TY - GEN

T1 - Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System

AU - Tu, Chia Hao

AU - Li, Chunshien

PY - 2018

Y1 - 2018

N2 - A complex-fuzzy machine learning approach to function approximation for multiple functions is proposed in this paper. The proposed approach involves the utility of complex-valued vector outputs by a novel complex-fuzzy model using complex fuzzy sets and the famous PSO-RLSE hybrid algorithm for machine learning of the model. An experiment was used to test the proposed approach for the ability of approximating four functions simultaneously. With the experimental result, the performance by the proposed model is promising and the proposed approach is compared to other methods. With complex fuzzy sets, the proposed approach has shown the excellent capability of function approximation for multiple functions using one single model with good performance.

AB - A complex-fuzzy machine learning approach to function approximation for multiple functions is proposed in this paper. The proposed approach involves the utility of complex-valued vector outputs by a novel complex-fuzzy model using complex fuzzy sets and the famous PSO-RLSE hybrid algorithm for machine learning of the model. An experiment was used to test the proposed approach for the ability of approximating four functions simultaneously. With the experimental result, the performance by the proposed model is promising and the proposed approach is compared to other methods. With complex fuzzy sets, the proposed approach has shown the excellent capability of function approximation for multiple functions using one single model with good performance.

KW - Complex fuzzy set

KW - Function approximation

KW - Fuzzy system

KW - Multi-target prediction

UR - https://www.scopus.com/pages/publications/85043525174

U2 - 10.1007/978-3-319-75417-8_23

DO - 10.1007/978-3-319-75417-8_23

M3 - 会议稿件

AN - SCOPUS:85043525174

SN - 9783319754161

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 243

EP - 254

BT - Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings

A2 - Pham, Hoang

A2 - Nguyen, Ngoc Thanh

A2 - Trawinski, Bogdan

A2 - Hoang, Duong Hung

A2 - Hong, Tzung-Pei

PB - Springer Verlag

T2 - 10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018

Y2 - 19 March 2018 through 21 March 2018

ER -

Tu CH, Li C. Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System. In Pham H, Nguyen NT, Trawinski B, Hoang DH, Hong TP, editors, Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings. Springer Verlag. 2018. p. 243-254. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-75417-8_23

Multiple Function Approximation - A New Approach Using Complex Fuzzy Inference System

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this