首页|期刊导航|哈尔滨工业大学学报（英文版）|Adaptive control of machining process based on extended entropy square error and wavelet neural network
哈尔滨工业大学学报（英文版）2007，Vol.14Issue(3)：349-353,5.
Adaptive control of machining process based on extended entropy square error and wavelet neural network
Adaptive control of machining process based on extended entropy square error and wavelet neural network
摘要
Abstract
Combining information entropy and wavelet analysis with neural network, an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error (EESE) and wavelet neural network (WNN). Extended entropy square error function is defined and its availability is proved theoretically. Replacing the mean square error criterion of BP algorithm with the EESE criterion, the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter, translating parameter of the wavelet and neural network weights. Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network. The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions, thus improving the machining efficiency and protecting the tool.关键词
machining process/adaptive control/extended entropy square error/wavelet neural networkKey words
machining process/adaptive control/extended entropy square error/wavelet neural network分类
信息技术与安全科学引用本文复制引用
LAI Xing-yu,YE Bang-yan,LI Wei-guang,YAN Chun-yan..Adaptive control of machining process based on extended entropy square error and wavelet neural network[J].哈尔滨工业大学学报（英文版）,2007,14(3):349-353,5.基金项目
Sponsored by the Natural Science Foundation of Guangdong Province ( Grant No. 06025546 ) and the National Natural Science Foundation of China ( Grant No. 50305005 ). （ Grant No. 06025546 ）
