Study on the Diagnosis of Internal Defects of Wood Based on MEA-BP Neural Network
In order to improve the automatic recognition rate of wood internal defects,Electrical Resistance Tomography (ERT) method was used to obtain the electrical conductivity fluctuation signal.Three-layer wavelet packet analysis is performed on the collected data by wavelet packet transform,and the 8 dimensional feature vector was extracted.The weight and threshold were optimized by using Mind Evolutionary Algorithm (MEA).Hole,knot and decay of the 45 groups of data for BP neural network training,20 sets for each defect was used as a test set,and the defects of wood were identified.The results showed that the recognition rates of MEABP neural network for wood holes,knots and decay were 96.92％,95.38％ and 92.31％.The model solves the optimization problem of complex combination,improves the search efficiency and achieves the best prediction effect.
NETL
NSTL
万方数据
维普
