A FUSION ALGORITHM BASED IMPROVED FUNCTION LINK ARTIFICIAL NEURAL NETWORK FOR LUMBER MOISTURE CONTENT (LMC) MEASURING

摘要：

Aimed to improve the measurement precision of the traditional microwave transmission method for lumber moisture content (LMC), this paper presents a dynamic compensation technique based on function link neural networks (FLNN).The microwave attenuation and phase shift are taken as the inputs of the dynamic compensation model.Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a combination learning algorithm using particle swarm optimization (PSO) and BP is adopted to train the neural network dynamic compensation model.It will enable the compensation process with an overall accuracy.Experimental results show that the use of the technology on lumber moisture content measurements for calibration is an effective method and has certain project value.

关键词： Lumber moisture content Function link neural network Particle swarm optimization

作者: MING-BAO LI JIA-WEI ZHANG SHI-QIANG ZHENG

作者单位: Northeast Forestry University, Harbin 150040, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2821-2825

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）