木材含水率的神经网络模型构建

Neural Network Model for Wood Moisture Content

摘要: 木材含水率是木材干燥过程中的重要参数。为了使木材干燥实验数据得到更加精确有效的处理，利用RMA神经网络模型算法的优势来构建木材含水率中影响因素与时间的函数关系。通过选取多层反馈神经网络，进行模型算法建立并针对已经完全建立好的模型进行运算平台建设；综合实际实验数据结合仿真程序得出木材含水率实验系统的解析表达式。对进一步研究木材干燥含水率系统函数具有十分重要意义。

Abstract: Wood moisture content is an important technical parameter in the wood drying process. In order to deal with data of the wood drying test more accurately and effectively, we decided to use the advantages of RMA neural network model to design an algorithm for wood moisture content in-fluencing factors and relationship of time. Firstly, we select a kind of neural network named Re-current Neural Network to set up a model algorithm. And next thing is building an operation pro-gram which can express the RMA neural network model well. Finally, we combine real empirical data with the program, and we get functional statements of systemic wood moisture content.