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| 基于小波神经网络的切削刀具状态监测 | |
| 引用本文： | 冯冀宁,刘彬,刁哲军,张春生.基于小波神经网络的切削刀具状态监测[J].中国机械工程,2004,15(4):321-324. |
| 作者姓名： | 冯冀宁 刘彬 刁哲军 张春生 |
| 作者单位： | 1. 河北师范大学电子工程系,石家庄,050031;燕山大学电气工程学院,秦皇岛,066004 2. 燕山大学电气工程学院,秦皇岛,066004 3. 河北师范大学电子工程系,石家庄,050031 4. 衡水师范高等专科学校计算机中心,衡水,053200 |
| 基金项目： | 河北省教育厅基金资助项目 (2 0 0 0 2 18) |
| 摘 要： | 提出了一种基于小波神经网络的切削刀具状态监测方法，即提取反映刀具磨损状态的特征参数，利用小波神经网络的非线性模型，实现在线状态监测；同时针对多输入输出问题带来的网络规模大、收敛速度慢等问题，提出了一种网络优化算法，即采用改进的遗传算法寻找最优小波基元，从而简化小波网络并加快收敛。仿真实例证明该方法是有效的。 |
| 关 键 词： | 刀具 状态监测 小波网络 优化的遗传算法 |
| 文章编号： | 1004-132X(2004)04-0321-04 |
Condition Detection on Cutting Tools Wavelet-Based Neural Network |
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| Feng Jining , Liu Bin Diao Zhejun Zhang Cunsheng .Hebei Normal University,Shijiazhuang, .Yanshan University,Qinhuangdao, . Hengshui Teachers Junior-college,Hengshui.Condition Detection on Cutting Tools Wavelet-Based Neural Network[J].China Mechanical Engineering,2004,15(4):321-324. | |
| Authors: | Feng Jining Liu Bin Diao Zhejun Zhang Cunsheng Hebei Normal University Shijiazhuang Yanshan University Qinhuangdao Hengshui Teachers Junior-college Hengshui |
| Affiliation: | Feng Jining 1,2 Liu Bin 2 Diao Zhejun 1 Zhang Cunsheng 3 1.Hebei Normal University,Shijiazhuang,050031 2.Yanshan University,Qinhuangdao,066004 3. Hengshui Teachers Junior-college,Hengshui,053200 |
| Abstract: | A condition detection method for cutting tools based on wavelet neural network(WNN) which collects multi-source feature parameters of cutting tools was proposed to realize on-line state detection based on the non-liner model of WNN.Then aiming at the problem of "MIMO" diagnosis system-the "dimension disaster" and the slow learning speed, the wavelet network is improved by optimization genetic algorithm in order to find the optimum wavelet neurons. Finally, the simpler structure and quickly convergent velocity of the new algorithm is demonstrated by simulation results. |
| Keywords: | cutting tool condition detection wavelet neural network optimum genetic algorithm |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |