基于小波神经网络的加工过程自适应控制  

Adaptive Control of Machining Process Based on Wavelet Neural Network

作　　者：鄢春艳[1] 赖兴余[1] 叶邦彦[2] 李伟光[2] 

机构地区：[1]广东省科技干部学院机电工程系,广州510640 [2]华南理工大学机械工程学院,广州510640

出　　处：《计算机工程与应用》2006年第7期214-217,共4页Computer Engineering and Applications

基　　金：国家自然科学基金资助项目(编号:50305005);广东省自然科学基金资助项目(编号:020917)

摘　　要：把信息熵、小波分析和神经网络相结合,提出了基于小波神经网络的加工过程自适应控制系统及其自适应控制算法。提出并定义了广义熵方误差函数,在理论上证明了广义熵方误差函数的有效性。用广义熵方误差函数准则取代BP算法的均方误差准则,用自适应地搜索小波基函数和自适应地调整小波的尺度参数、平移参数和神经网络权值的方法对参数变化的切削力进行在线控制。仿真结果表明,该系统响应快,无超调,比传统的加工过程神经网络自适应控制具有更好的控制效果。Combining information entropy,wavelet analysis with neural network,an adoptive control system and an adaptive control algorithm are presented for machining process based on wavelet neural network.Extended Entropy Square Error（EESE） function is defined and its effect 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 neural network.

关 键 词：小波神经网络 加工过程 自适应控制 广义熵方误差 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]