標題: Study on Least Trimmed Absolute Deviations Artificial Neural Network
作者: Liao, Shih-Hui
Chang, Jyh-Yeong
Lin, Chin-Teng
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: least trimmed sum of absolute deviations (LTA) estimator;artificial neural network (ANN);least trimmed sum of absolute deviations artificial neural network (LTA-ANN);particle swarm optimization (PSO);simplex method of Nelder and Mead (NM)
公開日期: 2013
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.
URI: http://hdl.handle.net/11536/24780
ISBN: 978-1-4799-0386-3
期刊: 2013 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY 2013)
起始頁: 156
結束頁: 160
Appears in Collections:Conferences Paper