A New Fuzzy Approach for Pattern Recognition with Application to EMG Classification

Yong-Sheng Yang; F. K. Lam; Francis H. Y. Chan; Yuan-Ting Zhang; Philip A. Parker

doi:10.1109/icnn.1996.549053

A New Fuzzy Approach for Pattern Recognition with Application to EMG Classification

Yong-Sheng Yang, F. K. Lam, Francis H. Y. Chan, Yuan-Ting Zhang, Philip A. Parker

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

10 Citations (Scopus)

Abstract

A fuzzy logic system with center average defuzzifier, product-inference rule, nonsingleton fuzzifier and Gauss membership function is discussed in this paper. The fuzzy sets are initially defined by the cluster parameters from the Basic ISO-DATA algorithm on input space. The system is then trained via back error propagation algorithm so that the fuzzy sets are fine-tuned. The system is applied to functional EMG classification and compared with its ANN counterpart. It is superior to the latter in at least three points: higher recognition rate; insensitive to over-training; and more consistent outputs thus having higher reliability.

Original language	English
Title of host publication	The 1996 IEEE International Conference on Neural Networks
Publisher	IEEE
Pages	1109-1114
Volume	2
ISBN (Print)	0780332105, 0780332113
DOIs	https://doi.org/10.1109/icnn.1996.549053
Publication status	Published - Jun 1996
Externally published	Yes
Event	1996 IEEE International Conference on Neural Networks (ICNN'96) - Sheraton Washington Hotel, Washington, United States Duration: 3 Jun 1996 → 6 Jun 1996

Conference

Conference	1996 IEEE International Conference on Neural Networks (ICNN'96)
Place	United States
City	Washington
Period	3/06/96 → 6/06/96

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@inproceedings{1372e2c8e7e549c79d4e20b0092603a7,

title = "A New Fuzzy Approach for Pattern Recognition with Application to EMG Classification",

abstract = "A fuzzy logic system with center average defuzzifier, product-inference rule, nonsingleton fuzzifier and Gauss membership function is discussed in this paper. The fuzzy sets are initially defined by the cluster parameters from the Basic ISO-DATA algorithm on input space. The system is then trained via back error propagation algorithm so that the fuzzy sets are fine-tuned. The system is applied to functional EMG classification and compared with its ANN counterpart. It is superior to the latter in at least three points: higher recognition rate; insensitive to over-training; and more consistent outputs thus having higher reliability.",

author = "Yong-Sheng Yang and Lam, {F. K.} and Chan, {Francis H. Y.} and Yuan-Ting Zhang and Parker, {Philip A.}",

year = "1996",

month = jun,

doi = "10.1109/icnn.1996.549053",

language = "English",

isbn = "0780332105",

volume = "2",

pages = "1109--1114",

booktitle = "The 1996 IEEE International Conference on Neural Networks",

publisher = "IEEE",

address = "United States",

note = "1996 IEEE International Conference on Neural Networks (ICNN'96) ; Conference date: 03-06-1996 Through 06-06-1996",

}

Yang, Y-S, Lam, FK, Chan, FHY, Zhang, Y-T & Parker, PA 1996, A New Fuzzy Approach for Pattern Recognition with Application to EMG Classification. in The 1996 IEEE International Conference on Neural Networks. vol. 2, IEEE, pp. 1109-1114, 1996 IEEE International Conference on Neural Networks (ICNN'96), Washington, District of Columbia, United States, 3/06/96. https://doi.org/10.1109/icnn.1996.549053

A New Fuzzy Approach for Pattern Recognition with Application to EMG Classification. / Yang, Yong-Sheng; Lam, F. K.; Chan, Francis H. Y. et al.
The 1996 IEEE International Conference on Neural Networks. Vol. 2 IEEE, 1996. p. 1109-1114.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A New Fuzzy Approach for Pattern Recognition with Application to EMG Classification

AU - Yang, Yong-Sheng

AU - Lam, F. K.

AU - Chan, Francis H. Y.

AU - Zhang, Yuan-Ting

AU - Parker, Philip A.

PY - 1996/6

Y1 - 1996/6

N2 - A fuzzy logic system with center average defuzzifier, product-inference rule, nonsingleton fuzzifier and Gauss membership function is discussed in this paper. The fuzzy sets are initially defined by the cluster parameters from the Basic ISO-DATA algorithm on input space. The system is then trained via back error propagation algorithm so that the fuzzy sets are fine-tuned. The system is applied to functional EMG classification and compared with its ANN counterpart. It is superior to the latter in at least three points: higher recognition rate; insensitive to over-training; and more consistent outputs thus having higher reliability.

AB - A fuzzy logic system with center average defuzzifier, product-inference rule, nonsingleton fuzzifier and Gauss membership function is discussed in this paper. The fuzzy sets are initially defined by the cluster parameters from the Basic ISO-DATA algorithm on input space. The system is then trained via back error propagation algorithm so that the fuzzy sets are fine-tuned. The system is applied to functional EMG classification and compared with its ANN counterpart. It is superior to the latter in at least three points: higher recognition rate; insensitive to over-training; and more consistent outputs thus having higher reliability.

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DO - 10.1109/icnn.1996.549053

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0780332105

SN - 0780332113

VL - 2

SP - 1109

EP - 1114

BT - The 1996 IEEE International Conference on Neural Networks

PB - IEEE

T2 - 1996 IEEE International Conference on Neural Networks (ICNN'96)

Y2 - 3 June 1996 through 6 June 1996

ER -

A New Fuzzy Approach for Pattern Recognition with Application to EMG Classification

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