Study on least trimmed squares fuzzy neural networks
Hsu-Kun Wu, Jer‐Guang Hsieh, Ker‐Wei Yu · 2010
In this paper, least trimmed squares (LTS) estimators, frequently used in robust (or resistant) linear parametric regression problems, will be generalized to nonparametric LTS-fuzzy neural networks (LTS-FNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) algorithms will be provided. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed LTS-FNNs. Simulation results show that the LTS-FNNs proposed in this paper have good robustness against outliers.