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.
|主出版物標題||2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY)|
|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 2013 → 8 十二月 2013
|Conference||International Conference on Fuzzy Theory and Its Applications (iFUZZY)|
|期間||6/12/13 → 8/12/13|