Ultrasonic technique based on neural networks in vehicle modulation recognition
Jinsong Tao, Chen Shixiu, Yang Li, Yaogai Hu · 2005
Adapted learning rate /spl eta/ according to the convergence pace has been used in back-propagation neural networks (BPNN). Several vehicle recognition methods are compared and ultrasonic sensor has been given. Vehicle's portrait section is acquired by ultrasonic sensor and the character been distilled as the BPNN's input. Three layers BPNN with 6 inputs, 12 hidden nodes and 3 outputs run in the vehicle recognition program, and 6 different kinds of vehicles can be most correctly distinguished. The application in practice shows its recognition rate achieved 93.2%. Data is stored in server to provide different use.