干燥过程中木材含水率神经网络预测模型

将人工神经网络应用于木材干燥控制研究中,建立可用于木材含水率预测的时延神经网络基准模型,并给出其网络辨识结构。通过3个树种的实际干燥数据对所建立的网络模型进行训练和验证,仿真结果表明预测模型是可行而有效的,具有较好的动态跟踪能力和预报特性,实现了木材干燥基准的数学模型化,对进一步优化木材干燥基准实施与控制具有重要的指导意义和应用价值。

Neural Network Prediction Model of Wood Moisture Content for Drying Process

The principal problems to realize fully automatic control of drying are mathematical modeling and carrying out drying schedule. The paper applied neural network to the research of wood drying control, established time-delay neural network schedule model useful to wood moisture content prediction, gave the identification structure of time-delay neural network and used practical drying data of three species to train and test the networks. The simulation results showed that the prediction model were feasible, effective, and had good ability of dynamic track and forecasting characteristics, which did not only realize mathematical modeling for drying schedule, but were significant to optimize schedule implementation and control of wood drying as well.