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Image classification with empirical mode decomposition and radial basis function networks in the dynamic weighing of vehicles

Finally, the average residual component. Rising slope of the mean output value from the stage to the output value increased 10% to 90% of the mean stage data points between the straight line slope of the fitted straight line, down the slope of the output value from the mean of 90% in stage stage to the output value of 10% of the mean data points between the straight line slope of the fitted straight line. Vj {{I {Xia Figure 7 different input test results comparing different modeling Figure 7 for the different input RBF network with different model test results curve, experimental models were chosen 3: trucks, forklifts, cranes, experiment 3 constant quality types of vehicles in a single training data for the 37 modeling groups (Crane l4 group, 13 groups of forklifts, trucks lO group), test data in 9 groups (6 groups of forklifts, trucks, group 3). Training data modeling in the classification of 24 groups (11 groups of forklifts, trucks l3 group), test data in 9 groups (6 groups of forklifts, trucks, group 3). Input in the choice of network parameters, the combination of the factors that affect vehicle dynamic weighing, the choice of front, rear axle through the platen,[link widoczny dla zalogowanych], the average speed, fully switched to weighing station weighing signal voltage when the mean rising slope of the signal coming to power, to step down the slope of the input signal node. The vehicle model for the study. When the vehicle is 8 input nodes, output nodes for the static weighing the vehicle the output voltage value, that is, the number of output neurons is 1. The test results from the curve of Figure 7 shows the same data to the average of EMD decomposition margin classification model as an input the test results, the maximum relative error occurs is 1.15%, while the simple average as an input single model to reach 7.05% test error, so the use of radial basis function (RBF) network for nonlinear modeling of the system to EMD decomposition margin classification as an input model than the direct input of the original signal the average of a single model as an input with higher test accuracy. 4 Conclusion extracted according to vehicle type car features top view, and according to vehicle type classification modeling. After weighing on the stage plate EMD signal decomposition, the average margin EMD decomposition RBF network input as a node. Simulation results show that the average margin to EMD decomposition of the classification model for the input than the average of the original signal directly into a single model with higher precision. [References] [1] He Shu new. Vehicle dynamic weighing technology history, current situation and prospects [J]. Foreign Highway, 2004,24 (6) :104-108 [2] Yao Entao, Ji Juan, Zhang Ming. Two-axle vehicle dynamic weight signal analysis method [J]. Sensor Technology, 2005,24 (12) :22-25 [3] on behalf of the Ru spring. Car running performance [M]. National Defence Industry Press, 2003 [4] RakhaHA. KatzB, A1-KalsyAF. Fieldevaluationofweigh-in-motionscreeningontruckweighstationoperations [J]. IEEEIn-telligentVehideSymposium. 2003,6:74-79 [5] Chens. Orthogonalleastsquarelearningalgorithmforradialbasisfunctionnetworks [J]. IEEETram. OilNN, 1992,2 (2): 3023O9 [6] ChouCP, ChenYH, ChenIC. Integratingweigh-in-motionandelectronictollcollectionforcommercialvehicleoperationinTaiwan [J]. JournaloftheInstitutionofElectricalEngineers. 2004.44 (2) :73-89 [7] Xu Dong, Wu Zheng. Based MATLAB6. X, System Analysis and Design of a Neural Network [M]. Xi'an: Xidian University Press, 2002 [8] BenekohalR, E1-ZohairyY, ForrlerE, eta1. Truckdelayandtrafficconflictsaroundweighstations [J]. Tram'portationRe-searchRecord ,1999.1653:52-60

Image classification with empirical mode decomposition and radial basis function networks in the dynamic weighing of vehicles

Finally, the average residual component. Rising slope of the mean output value from the stage to the output value increased 10% to 90% of the mean stage data points between the straight line slope of the fitted straight line, down the slope of the output value from the mean of 90% in stage stage to the output value of 10% of the mean data points between the straight line slope of the fitted straight line. Vj {{I {Xia Figure 7 different input test results comparing different modeling Figure 7 for the different input RBF network with different model test results curve, experimental models were chosen 3: trucks, forklifts, cranes, experiment 3 constant quality types of vehicles in a single training data for the 37 modeling groups (Crane l4 group,[link widoczny dla zalogowanych], 13 groups of forklifts, trucks lO group),[link widoczny dla zalogowanych], test data in 9 groups (6 groups of forklifts, trucks, group 3). Training data modeling in the classification of 24 groups (11 groups of forklifts, trucks l3 group), test data in 9 groups (6 groups of forklifts, trucks, group 3). Input in the choice of network parameters, the combination of the factors that affect vehicle dynamic weighing, the choice of front, rear axle through the platen,[link widoczny dla zalogowanych], the average speed, fully switched to weighing station weighing signal voltage when the mean rising slope of the signal coming to power, to step down the slope of the input signal node. The vehicle model for the study. When the vehicle is 8 input nodes, output nodes for the static weighing the vehicle the output voltage value, that is, the number of output neurons is 1. The test results from the curve of Figure 7 shows the same data to the average of EMD decomposition margin classification model as an input the test results, the maximum relative error occurs is 1.15%, while the simple average as an input single model to reach 7.05% test error, so the use of radial basis function (RBF) network for nonlinear modeling of the system to EMD decomposition margin classification as an input model than the direct input of the original signal the average of a single model as an input with higher test accuracy. 4 Conclusion extracted according to vehicle type car features top view, and according to vehicle type classification modeling. After weighing on the stage plate EMD signal decomposition, the average margin EMD decomposition RBF network input as a node. Simulation results show that the average margin to EMD decomposition of the classification model for the input than the average of the original signal directly into a single model with higher precision. [References] [1] He Shu new. Vehicle dynamic weighing technology history, current situation and prospects [J]. Foreign Highway, 2004,24 (6) :104-108 [2] Yao Entao, Ji Juan, Zhang Ming. Two-axle vehicle dynamic weight signal analysis method [J]. Sensor Technology, 2005,24 (12) :22-25 [3] on behalf of the Ru spring. Car running performance [M]. National Defence Industry Press,[link widoczny dla zalogowanych], 2003 [4] RakhaHA. KatzB, A1-KalsyAF. Fieldevaluationofweigh-in-motionscreeningontruckweighstationoperations [J]. IEEEIn-telligentVehideSymposium. 2003,6:74-79 [5] Chens. Orthogonalleastsquarelearningalgorithmforradialbasisfunctionnetworks [J]. IEEETram. OilNN, 1992,2 (2): 3023O9 [6] ChouCP, ChenYH, ChenIC. Integratingweigh-in-motionandelectronictollcollectionforcommercialvehicleoperationinTaiwan [J]. JournaloftheInstitutionofElectricalEngineers. 2004.44 (2) :73-89 [7] Xu Dong, Wu Zheng. Based MATLAB6. X, System Analysis and Design of a Neural Network [M]. Xi'an: Xidian University Press, 2002 [8] BenekohalR, E1-ZohairyY, ForrlerE, eta1. Truckdelayandtrafficconflictsaroundweighstations [J]. Tram'portationRe-searchRecord ,1999.1653:52-60
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