A Fusion Algorithm Based Improved Function Link Artificial Neural Network for Lumber Moisture Content (LMC) Measuring

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.