Shih-Yung Wei

TL;DR: This investigation presents a SVR model with chaotic genetic algorithm (CGA), namely SVRCGA, to forecast the tourism demands, and empirical results that involve tourism demands data from existed paper reveal the proposed SVRC GA model outperforms other approaches in the literature.

TL;DR: The forecasting results indicate that the proposed model yields more accurate forecasting results than the ARIMA and TF-e-SVR-SA models, and therefore the SSVRCIA model is a promising alternative for electric load forecasting.

TL;DR: The proposed CGA, based on the chaotic optimization algorithm and GA, employs internal randomness of chaos iterations to overcome premature local optimum of GA in determining parameters of a SVR model to improve the forecasting performance.

TL;DR: Based on dynamic panel data of listed companies in the Taiwan electronics industry from 2006 to 2017, the authors used the GMM estimation method to examine the factors influencing intelligent capital on enterprise performance and found that human capital, innovation capital, process capital, and customer capital all significantly improve enterprise performance through the mechanism of organizational learning.

TL;DR: This investigation presents a traffic flow forecasting model by employing seasonal adjustment to deal with the cyclic (seasonal) traffic flow, in addition, the chaotic simulated annealing algorithm is also applied to optimize the three parameters of a SVR model, namely SSVRCSA, to forecast inter-urban traffic flow.