Study on Least Trimmed Absolute Deviations Artificial Neural Network

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

*Corresponding author for this work

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this paper, the least trimmed sum of absolute deviations (LTA) estimator, frequently used in robust linear parametric regression problems, will be generalized to nonparametric least trimmed sum of absolute deviations-artificial neural network (LTA-ANN) for nonlinear regression problems. In linear parametric regression problems, the LTA estimator usually have good robustness against outliers and can theoretically tolerate up to 50% of outlying data. Moreover, a nonderivative hybrid method mixing the simplex method of Nelder and Mead (NM) and particle swarm optimization algorithm (PSO), abbreviated as SNM-PSO, will be provided in this study for the training of the parameters of LTA-ANN. Some numerical examples will be provided to compare the robustness against outliers for usual artificial neural network (ANN) and the proposed LTA-ANN. Simulation results show that the LTA-ANN proposed in this paper have good robustness against outliers.
原文English
主出版物標題2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY)
頁面156-160
頁數5
DOIs
出版狀態Published - 6 十二月 2013
事件International Conference on Fuzzy Theory and Its Applications (iFUZZY) - Taipei, Taiwan
持續時間: 6 十二月 20138 十二月 2013

Conference

ConferenceInternational Conference on Fuzzy Theory and Its Applications (iFUZZY)
國家Taiwan
城市Taipei
期間6/12/138/12/13

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