Application of Wavelet Neural Networks for Recognizing the Patterns of Wood Inner Defects

Ultrasonic nondestructive testing for wood defects is studied based on the energy spectrum variety of the ultrasonic signals by means of wavelet transform, coefficient of wavelet node and the artificial neural networks (ANN). The energy change of defect wood specimen mostly depends on the degree of defects. And the defect degree is proportional to the energy change. By comparing the energy variety of every signal crunode in the 5^th layer wavelet bundle, it is explicit that the variety of the crunode (5,0) among 32 crunodes is the biggest. And the crunode contains defect character information mostly. The energy varieties of 32 crunodes in the 5^th layer and wavelet radix of (5,0) crunode are respectively regarded as the character inputs of the ANN. The identifying results show that taking wavelet radix of (5,0) crunode as the character input is effective in recognizing the patterns of wood inner defects.