Q
Qifa Xu
Researcher at Hefei University of Technology
Publications - 46
Citations - 928
Qifa Xu is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Quantile regression & Quantile. The author has an hindex of 14, co-authored 45 publications receiving 557 citations. Previous affiliations of Qifa Xu include Chinese Ministry of Education.
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Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function
TL;DR: For quantifying uncertainty associated with power load and obtaining more information of future load, a probability density forecasting method based on quantile regression neural network using triangle kernel function (QRNNT) is proposed.
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Composite quantile regression neural network with applications
TL;DR: The method provides an idea to bridge the gap between composite quantile regression and intelligent methods such as ANNs, SVM, etc., which is helpful to improve their robustness, goodness-of-fit and predictive ability.
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Reservoir flood control operation based on chaotic particle swarm optimization algorithm
TL;DR: In this paper, a chaotic particle swarm optimization (CPSO) algorithm based on the improved logistic map is presented, which uses the discharge flow process as the decision variables combined with the death penalty function.
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Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning
TL;DR: This work develops a novel framework through combined use of multi-domain vibration feature extraction, feature selection and cost-sensitive learning method that consistently outperforms the traditional classifiers such as support vector machine (SVM), gradient boosting decision tree (GBDT), etc.
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Quantile autoregression neural network model with applications to evaluating value at risk
TL;DR: The proposed QARNN model is flexible and can be used to explore potential nonlinear relationships among quantiles in time series data by optimizing an approximate error function and standard gradient based optimization algorithms.