DocumentCode :
2931627
Title :
Adaptive least trimmed squares fuzzy neural network
Author :
Jyh-Yeong Chang ; Shih-Hui Liao ; Chin-Teng Lin
Author_Institution :
Dept. of Electr. & Control Eng., Nat. Chiao-Tung Univ., Hsinchu, Taiwan
fYear :
2012
fDate :
16-18 Nov. 2012
Firstpage :
413
Lastpage :
416
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 :
fuzzy neural nets; learning (artificial intelligence); least squares approximations; ALTS-FNN; adaptive least trimmed squares fuzzy neural network; outlier detection; robustness; scale estimate; Adaptive systems; Backpropagation; Educational institutions; Function approximation; Fuzzy neural networks; Robustness; adaptive least trimmed squares fuzzy neural network (ALTS-FNN); fuzzy neural network (FNN); least trimmed squares (LTS) estimator; least trimmed squares fuzzy neural network (LTS-FNN);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Theory and it's Applications (iFUZZY), 2012 International Conference on
Conference_Location :
Taichung
Print_ISBN :
978-1-4673-2057-3
Type :
conf
DOI :
10.1109/iFUZZY.2012.6409741
Filename :
6409741
Link To Document :
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