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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...
Nonparametric Wilcoxon regressors, which generalize the rank-based Wilcoxon approach for linear parametric regression problems to nonparametric neural networks, were recently developed aiming at improving robustness against outliers in nonlinear regression problems. It is natural to investigate if the Wilcoxon approach can also be generalized to nonparametric classification problems. Motivated by...
The hexapod bio-robot possess significant advantages on uneven surfaces compared with other type locomotion systems. It has the ability of adapting itself to complex terrains in the way of imitating walking insects. A large computational capability, real-time control and advanced control strategy is required to coordinate many degrees-of-freedom (DOF) used in legged locomotion systems. In this paper...
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