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• W3093085523 startingPage "1732" @default.
• W3093085523 abstract "The adaptive neurofuzzy inference system (ANFIS) is a structured multioutput learning machine that has been successfully adopted in learning problems without noise or outliers. However, it does not work well for learning problems with noise or outliers. High-accuracy real-time forecasting of traffic flow is extremely difficult due to the effect of noise or outliers from complex traffic conditions. In this study, a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), is proposed to capture the correlations among traffic flow data and, thereby, improve the accuracy of traffic flow forecasting. The new learning system adopts a fantastic objective function that minimizes both the mean and the variance of the model bias. The results from an experiment based on real-world traffic flow data showed that, compared with some kernel-based approaches, neural network approaches, and conventional ANFIS learning systems, the proposed probabilistic R-ELANFIS achieves competitive performance in terms of forecasting ability and generalizability." @default.
• W3093085523 created "2020-10-22" @default.
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• W3093085523 date "2023-04-01" @default.
• W3093085523 modified "2023-10-17" @default.
• W3093085523 title "Probabilistic Regularized Extreme Learning for Robust Modeling of Traffic Flow Forecasting" @default.
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• W3093085523 doi "https://doi.org/10.1109/tnnls.2020.3027822" @default.
• W3093085523 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33064658" @default.
• W3093085523 hasPublicationYear "2023" @default.
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