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Wei-Chiang Hong
Researcher at Oriental Institute of Technology
Publications - 147
Citations - 8624
Wei-Chiang Hong is an academic researcher from Oriental Institute of Technology. The author has contributed to research in topics: Support vector machine & Autoregressive integrated moving average. The author has an hindex of 46, co-authored 129 publications receiving 6822 citations. Previous affiliations of Wei-Chiang Hong include Dayeh University & Jiangsu Normal University.
Papers
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Consensus models for AHP group decision making under row geometric mean prioritization method
TL;DR: This paper defines the consensus indexes to measure consensus degree among judgement matrices (or decision makers) for the AHP group decision making using RGMM and presents two AHP consensus models under RGMM, which satisfy the Pareto principle of social choice theory.
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Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms
Ping-Feng Pai,Wei-Chiang Hong +1 more
TL;DR: In this article, a recurrent support vector machines with genetic algorithms (RSVMG) is proposed to forecast electricity load, which is a promising alternative for forecasting electricity load in power industry.
Posted Content
Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting
Wei-Chiang Hong,Wei-Chiang Hong +1 more
TL;DR: In this article, a support vector machine with simulated annealing (SVMSA) model was proposed to forecast the electricity load in Taiwan, which outperformed the other two models, namely the autoregressive integrated moving average (ARIMA) model and the general regression neural networks (GRNN) model.
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Support vector machines with simulated annealing algorithms in electricity load forecasting
Ping-Feng Pai,Wei-Chiang Hong +1 more
TL;DR: In this paper, a support vector machine with simulated annealing (SVMSA) model was proposed to forecast the electricity load in Taiwan, which outperformed the other two models, namely the autoregressive integrated moving average (ARIMA) model and the general regression neural networks (GRNN) model.
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Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model
TL;DR: This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model and outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA).