Abstract
Wood moisture content (WMC) is an important technical index used in the wood drying process, and assessing its change accurately and reliably is the key to improving wood drying quality. In order to improve the accuracy and reliability of WMC forecasting, a modeling method is proposed that uses a modified ant colony algorithm (MACA) to optimize the least square support vector machine (LSSVM). The MACA combines the large-step size global search with the small-step size local fine search to obtain the optimal parameter combination automatically and are tested by five standard functions. Then the MACA-LSSVM model is proposed to predict the WMC and compared with back propagation neural network (BP-NN), LSSVM model, and ant colony optimization LSSVM (ACO-LSSVM). The drying data from a small-sized wood drying kiln independently developed by Northeast Forestry University are taken as the samples for analyzing. The results indicate that the root mean square relative error (RMSRE) obtained by the proposed method (MACA-LSSVM) is only 1.82%, which is 0.77%, 0.50%, and 0.20% less than those of the BP-NN, LSSVM, and ACO-LSSVM models. The forecasting time are 0.0070 s, 0.0030 s, and 0.0010 s shorter, respectively. The relative error (RE) and the mean absolute error (MAE) are also lower than those of the latter three models. The MACA-LSSVM shows the characteristics of low computational complexity, fast convergence speed, high prediction accuracy and strong generalization ability, and the prediction effect is ideal. This model can provide the theoretical support for intelligent control of the wood drying process.
Highlights
Wood is a kind of green material which is renewable and recyclable
The results show that the model can well fit the nonlinear relationship between the drying medium and the Wood moisture content (WMC). (2) The modified ant colony algorithm (MACA)-least square support vector machine (LSSVM) model has a higher forecasting accuracy than other three models (BP-NN, LSSVM, and Ant colony optimization (ACO)-LSSVM) and can meet the actual needs
The results indicate that the MACA is suitable for the optimization selection of LSSVM model parameters with fast convergence speed and high computational efficiency
Summary
Wood is a kind of green material which is renewable and recyclable. Wood products belong to a basic industry in national economy and occupy an important position. Jiang et al [18] employed LSSVM to establish a soft measurement model of moisture content in wood drying process and used PSO algorithm to optimize its parameters. Fernandez-Lozano et al [21] proposed the genetic algorithm to optimize the LSSVM parameters It is independent of the mathematical model of the problem, its genetic operation is relatively complex and the late convergence speed is slow [22]. The MACA-LSSVM is applied to forecast the WMC to improve the prediction accuracy and operation efficiency, which can ensure the quality of wood product, save energy, and reduce the cost. When solving problems, compared with the original method, the complexity of the model is reduced effectively, and the operation efficiency and convergence accuracy are improved [29]. I=1 where xi, xj and α are the input sample vector, the kernel function centre of RBF, and the width parameter of the RBF kernel function, respectively
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