Recognition of Wood-floor Damages Based on Wavelet Transform and Neural Network Ensemble
Analyzed the forced vibration dynamic characteristics of three different damage-types woodfloors, according to the characteristics of forced vibration signals, wavelet packet decompose was proposed to extract the Information related to the condition of the woodfloor materials from the data and served as characteristic parameters to be putted into neural network ensemble. The different damage-types of wood-floor can be recognized by artificial neural network ensemble if the reasonable artificial neural network ensemble model was chosen. The results show that the method of extracting the feature and the neural network ensemble model are effective for identifying the wood-floor damages. And, the recognition of the neural network ensemble is more accurate than that of single network classifier for wood-floor damage.
nepal network ensemble wavelet transform damage recognition feature extraction
Zhao Jian Zhao Dong
School of technology, Beijing Forestry University, Beijing, 100083,China
国际会议
长沙
英文
2158-2161
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）