RETRACTED: Intelligent color matching model for wood dyeing using Genetic Algorithm and Extreme learning machine

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.

Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

“…From the technology-learner perspective, intelligent learning combines technology and learners, using intelligent technology, carrying out intelligent learning activities, allowing the learning subject to participate in the learning process actively, and cultivating intelligent talents; literature [14] constructs an intelligent learning model from the multi-dimensional aspects of teaching methods, learning psychology, learning attitudes, etc., to promote learners' learning motivation and enthusiasm, and improve learners' learning ability; literature [15] puts forward the intelligent learning environment based on network mobile terminals, which can not only develop learners' learning space, but also improve learners' ability to cultivate learning; literature [16] believes that intelligent learning activities can be carried out to make learners actively participate in the learning process, and cultivate intelligent talents. Cluster analysis [19] is an unsupervised learning machine learning method that divides the objects in a dataset into different groups or "clusters" so that objects within the same group have a higher degree of similarity. In comparison, objects between different groups have a lower degree of similarity.…”