摘要
The primary purpose is to develop a robust adaptive machine parts recognitionsystem. A fuzzy neural network classifier is proposed for machine parts classifier. It is anefficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzyneural network classifier is presented based on fuzzy mapping model. It is used for machine partsclassification. The experimental system of machine parts classification is introduced. A robustleast square back-propagation (RLSBP) training algorithm which combines robust least square (RLS)with back-propagation (BP) algorithm is put forward. Simulation and experimental results show thatthe learning property of RLSBP is superior to BP.
The primary purpose is to develop a robust adaptive machine parts recognitionsystem. A fuzzy neural network classifier is proposed for machine parts classifier. It is anefficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzyneural network classifier is presented based on fuzzy mapping model. It is used for machine partsclassification. The experimental system of machine parts classification is introduced. A robustleast square back-propagation (RLSBP) training algorithm which combines robust least square (RLS)with back-propagation (BP) algorithm is put forward. Simulation and experimental results show thatthe learning property of RLSBP is superior to BP.
基金
The project is supported by National Natural Science Foundation of China (No.50275100)
Opening Foundation of the State Education Ministry Laboratory of Image Information
Intelligence Control of Huazhong University of Science
Technology, China (N
