Adaptive Least Trimmed Squares Fuzzy Neural Network

Jyh-Yeong Chang; Shih-Hui Liao; Chin-Teng Lin

doi:10.1109/iFUZZY.2012.6409741

Adaptive Least Trimmed Squares Fuzzy Neural Network

Jyh-Yeong Chang, Shih-Hui Liao, Chin-Teng Lin

Department of Electrical and Computer Engineering

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

2 Scopus citations

Abstract

In this paper, we propose the adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies the scale estimate to the least trimmed squares fuzzy neural network (LTS-FNN). The emphasis of this paper is particular on the robustness against the outliers and the choice of the trimming constant can be determined adaptively. Some numerical examples will be provided to compare the robustness against outliers for usual FNN and the ALTS-FNN. Simulation results show that the ALTS-FNN in the paper have good performance for outlier detection.

Original language	English
Title of host publication	2012 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY2012)
Publisher	IEEE
Pages	413-416
Number of pages	4
ISBN (Print)	978-1-4673-2056-6
DOIs	https://doi.org/10.1109/iFUZZY.2012.6409741
State	Published - 2012

Keywords

least trimmed squares (LTS) estimator
fuzzy neural network (FNN)
least trimmed squares fuzzy neural network (LTS-FNN)
adaptive least trimmed squares fuzzy neural network (ALTS-FNN)

Access to Document

10.1109/iFUZZY.2012.6409741

Fingerprint Dive into the research topics of 'Adaptive Least Trimmed Squares Fuzzy Neural Network'. Together they form a unique fingerprint.

Cite this

@inproceedings{904375315a3f45aa99715c9f5d101739,

title = "Adaptive Least Trimmed Squares Fuzzy Neural Network",

abstract = "In this paper, we propose the adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies the scale estimate to the least trimmed squares fuzzy neural network (LTS-FNN). The emphasis of this paper is particular on the robustness against the outliers and the choice of the trimming constant can be determined adaptively. Some numerical examples will be provided to compare the robustness against outliers for usual FNN and the ALTS-FNN. Simulation results show that the ALTS-FNN in the paper have good performance for outlier detection.",

keywords = "least trimmed squares (LTS) estimator, fuzzy neural network (FNN), least trimmed squares fuzzy neural network (LTS-FNN), adaptive least trimmed squares fuzzy neural network (ALTS-FNN)",

author = "Jyh-Yeong Chang and Shih-Hui Liao and Chin-Teng Lin",

year = "2012",

doi = "10.1109/iFUZZY.2012.6409741",

language = "English",

isbn = "978-1-4673-2056-6",

pages = "413--416",

booktitle = "2012 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY2012)",

publisher = "IEEE",

}

TY - GEN

T1 - Adaptive Least Trimmed Squares Fuzzy Neural Network

AU - Chang, Jyh-Yeong

AU - Liao, Shih-Hui

AU - Lin, Chin-Teng

PY - 2012

Y1 - 2012

N2 - In this paper, we propose the adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies the scale estimate to the least trimmed squares fuzzy neural network (LTS-FNN). The emphasis of this paper is particular on the robustness against the outliers and the choice of the trimming constant can be determined adaptively. Some numerical examples will be provided to compare the robustness against outliers for usual FNN and the ALTS-FNN. Simulation results show that the ALTS-FNN in the paper have good performance for outlier detection.

AB - In this paper, we propose the adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies the scale estimate to the least trimmed squares fuzzy neural network (LTS-FNN). The emphasis of this paper is particular on the robustness against the outliers and the choice of the trimming constant can be determined adaptively. Some numerical examples will be provided to compare the robustness against outliers for usual FNN and the ALTS-FNN. Simulation results show that the ALTS-FNN in the paper have good performance for outlier detection.

KW - least trimmed squares (LTS) estimator

KW - fuzzy neural network (FNN)

KW - least trimmed squares fuzzy neural network (LTS-FNN)

KW - adaptive least trimmed squares fuzzy neural network (ALTS-FNN)

U2 - 10.1109/iFUZZY.2012.6409741

DO - 10.1109/iFUZZY.2012.6409741

M3 - Conference contribution

SN - 978-1-4673-2056-6

SP - 413

EP - 416

BT - 2012 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY2012)

PB - IEEE

ER -