Shufang Song^1,*, Ruyang He¹, Zhaoyin Shi¹, Weiya Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 65-85, 2021, DOI:10.32604/cmes.2021.015378 - 28 June 2021

Abstract The variable importance measure (VIM) can be implemented to rank or select important variables, which can effectively reduce the variable dimension and shorten the computational time. Random forest (RF) is an ensemble learning method by constructing multiple decision trees. In order to improve the prediction accuracy of random forest, advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees. Referring to the Mean Decrease Accuracy (MDA) index based on Out-of-Bag (OOB) data, the single variable, group variables and correlated variables importance measures are proposed to More >

Lu Li¹, Yuzhen Fan², Xinglang Su^1,*, Gefei Qiu¹

Energy Engineering, Vol.118, No.3, pp. 565-580, 2021, DOI:10.32604/EE.2021.014627 - 22 March 2021

Abstract Because of the randomness and uncertainty, integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow. This paper uses polynomial normal transformation method to deal with non-normal random variable correlation, and solves probabilistic load flow based on Kriging method. This method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear combination. Compared with traditional approaches which need a greater number of calculation times, long simulation time, and large memory space, Kriging More >

Yaohui Li^1,2, Yizhong Wu^1,3, Zhengdong Huang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.97, No.4, pp. 323-357, 2014, DOI:10.3970/cmes.2014.097.323

Abstract Kriging model, which provides an exact interpolation and minimizes the error estimates, is a highly-precise global approximation model in contrast with other traditional response surfaces. Therefore, sequential exploratory experimental design (SEED) with Kriging model is crucial for globally approximating a complex black-box function. However, the more sampling points are, the longer time it would take to update the Kriging model during sequential exploratory design. This paper, therefore, proposes a new construction method called incremental Kriging method (IKM) to improve the constructing efficiency with just a little and controllable loss of accuracy for Kriging model. The… More >

Zichun Yang^1,2,3, Wencai Sun²

CMES-Computer Modeling in Engineering & Sciences, Vol.92, No.2, pp. 193-212, 2013, DOI:10.3970/cmes.2013.092.193

Abstract The set-based structural eigenvalue problem is defined, by expressing the uncertainties of the structural parameters in terms of various convex sets. A new method based on Kriging model and Particle Swarm Optimization (PSO) is proposed for solving this problem. The introduction of the Kriging model into this approach can effectively reduce the computational burden especially for largescale structures. The solutions of the non-linear and non-monotonic problems are more accurate than those obtained by other methods in the literature with the PSO algorithm. The experimental points for Kriging model are sampled according to Latin hypercube sampling More >