An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive ...An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive learning rate and a fixed momentum factor is developed to train back-propagation neural network for accurate and efficient defects classifications. Detection results of rolling scar defects show that such detection system can achieve accurate positioning to defects edges for its improved noise suppression. More precise characteristic parameters of defects can also be extracted.Furthermore,defects classification is adopted to remedy the limitations of low convergence rate and local minimum. It can also attain the optimal training precision of 0. 00926 with the least 96 iterations. Finally,an enhanced identification rate of 95% has been confirmed for defects by using the detection system. It will also be positive in producing high-quality steel rails and guaranteeing the national transport safety.展开更多
On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative fe...On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative feedback of the neural cell in the output layer, the cell in the input layer excites the corresponding cell in the ontput layer meanwhile it inhibits the lateral cells. The network has its advantage on the processing of sound wave. In addition to filter the noise, it can search the significance frequency segments （Barks）. The ＂channel suppresser＂ feature, the special phenomena of the human ear, is explained based on the model. The learning algorithm of the network model is discussed, too. In the end, an example is introduced about the application of the network.展开更多
基金Supported by the National Natural Science Foundation of China(No.51174151)the Key Scientific Research Project of Education Department of Hubei Province(No.D20151102)the Key Scientific and Technological Project of Wuhan Technology Bureau(No.2014010202010088)
文摘An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive learning rate and a fixed momentum factor is developed to train back-propagation neural network for accurate and efficient defects classifications. Detection results of rolling scar defects show that such detection system can achieve accurate positioning to defects edges for its improved noise suppression. More precise characteristic parameters of defects can also be extracted.Furthermore,defects classification is adopted to remedy the limitations of low convergence rate and local minimum. It can also attain the optimal training precision of 0. 00926 with the least 96 iterations. Finally,an enhanced identification rate of 95% has been confirmed for defects by using the detection system. It will also be positive in producing high-quality steel rails and guaranteeing the national transport safety.
基金Shanghai Natural Research Foundation (No.06dz15003)
文摘On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative feedback of the neural cell in the output layer, the cell in the input layer excites the corresponding cell in the ontput layer meanwhile it inhibits the lateral cells. The network has its advantage on the processing of sound wave. In addition to filter the noise, it can search the significance frequency segments （Barks）. The ＂channel suppresser＂ feature, the special phenomena of the human ear, is explained based on the model. The learning algorithm of the network model is discussed, too. In the end, an example is introduced about the application of the network.
