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Article type: Research Article
Authors: Guan, Xuemei | Li, Wenfeng; * | Huang, Qinglong | Huang, Jingyi
Affiliations: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, P.R. China
Correspondence: [*] Corresponding author. Wenfeng Li, College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, P.R. China. E-mail: [email protected].
Abstract: Wood dyeing technology is of great significance to improve the utilization rate of inferior wood resources. The challenge to imitating precious wood species by inferior wood is to quickly and accurately obtain the dyeing formula of precious wood species. This study uses Genetic Algorithm (GA) to optimize Extreme learning machine (ELM), and then a predictive model based on GA-ELM is proposed for predicting the dyeing formula of precious wood species. The sum of the relative deviations of the three dyes between the predicted formula and the actual formula, that is, the relative deviation of the formula, is calculated to evaluate the model’s prediction accuracy. The simulation results show that the average relative deviations of the formula predicted by Back Propagation (BP) neural network, Radial Basis Function (RBF) neural network, ELM, and GA-ELM are 0.808, 0.717, 0.708, and 0.262. The prediction deviation of the GA-ELM is much smaller than that of other traditional neural networks, which can achieve good results in wood production.
Keywords: Extreme learning machine, genetic algorithm, computer color matching, wood veneer
DOI: 10.3233/JIFS-210618
Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 4907-4917, 2022
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