Enhancing Extreme Learning Machine Robustness via Residual-Variance-Aware Dynamic Weighting and Broyden–Fletcher–Goldfarb–Shanno Optimization: Application to Metro Crowd Flow Prediction

Enhancing Extreme Learning Machine Robustness via Residual-Variance-Aware Dynamic Weighting and Broyden–Fletcher–Goldfarb–Shanno Optimization: Application to Metro Crowd Flow Prediction

Aiming at the robustness problem of the extreme learning machine (ELM) in noisy and nonuniform data scenarios, this paper proposes an improved algorithm (BFGS-URWELM) that integrates uniform residual weighting and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton optimization. This method introdu...

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Bibliographic Details
Main Authors: Lihui Wang, Jianguang Xie
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Systems
Subjects:
extreme learning machine
subway short-term passenger flow prediction
uniform residual weighting
BFGS
dynamic weighting mechanism
Online Access:https://www.mdpi.com/2079-8954/13/5/349
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