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The qualities of new data used in the sequential learning phase of the online sequential extreme learning machine algorithm (OS-ELM) have a significant impact on the performance of OS-ELM. This paper proposes a novel data filter mechanism for OS-ELM from the perspective of fuzziness and a fuzziness-based online sequential extreme learning machine algorithm (FOS-ELM). In FOS-ELM, when new data arrive, a fuzzy classifier first picks out the meaningful data according to the fuzziness of each sample. Specifically, the new samples with high-output fuzziness are selected and then used in sequential learning. The experimental results on eight binary classification problems and three multiclass classification problems have shown that FOS-ELM updated by the new samples with high-output fuzziness has better generalization performance than OS-ELM. Since the unimportant data are discarded before sequential learning, FOS-ELM can save more memory and have higher computational efficiency. In addition, FOS-ELM can handle data one-by-one or chunk-by-chunk with fixed or varying sizes. The relationship between the fuzziness of new samples and the model performance is also studied in this paper, which is expected to provide some useful guidelines for improving the generalization ability of online sequential learning algorithms. [ABSTRACT FROM AUTHOR]

The fuzzy user equilibrium problem in urban traffic assignment has attracted much attention since its great theoretical significance and wide application. Based on the fact that travelers tend to choose the minimum-cost path between every origin-destination pair of the traffic network, an equilibrium is emerging over time. However, in the real world, travelers’ selection of paths is often fuzzy with the lack of global information. In this paper, by aid of the Physarum polycephalum algorithm, we propose a model for solving the fuzzy user equilibrium problem. P. polycephalum can build a bio-network and assign the flow according to the location and the size of the food source. Taking full advantage of this feature, the proposed model associates the traffic demand with the food source and unifies the bio-network and the traffic network. The solution of the fuzzy user equilibrium problem is the flow assignment in the bio-network. To test the performance of the proposed method, we conduct experiments on some traffic networks selected from recent related works. The results show that the proposed method is efficient. [ABSTRACT FROM AUTHOR]