標題: Study on Adaptive Least Trimmed Squares Fuzzy Neural Network
作者: Liao, Shih-Hui
Han, Ming-Feng
Chang, Jyh-Yeong
Lin, Chin-Teng
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: 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)
公開日期: 1-Sep-2013
摘要: In the largest samplings of data, outliers are observations that are well separated from the major samples. To deal with outlier problems, a least trimmed squares (LTS) estimator is developed for robust linear regression problems. It is meaningful to generalize the LTS estimator to fuzzy neural network (FNN) for robust nonlinear regression problems. In addition, the determination of the trimming constant is important when using the LTS estimator. In this paper, we propose the use of an adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies a scale estimate to a LTS-FNN. This paper particularly emphasizes the robustness of the proposed network against outliers and an automatic determination of the trimming percentage. Simulation problems are provided to compare the performance of the proposed ALTS-FNN, with an LTS-FNN and typical FNN. Simulation results show that the proposed ALTS-FNN is highly robust against outliers.
URI: http://hdl.handle.net/11536/23031
ISSN: 1562-2479
期刊: INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
Volume: 15
Issue: 3
起始頁: 326
結束頁: 334
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