摘要
量子傅里叶变换是量子算法的基础,也是指数式效率的关键。提出了一种基于量子傅里叶变换的特征提取算法,该算法搭建了量子计算的运行路线;构建了实施量子傅里叶变换的特征提取步骤,并构造了峰值评价函数,用于评价提取出的特征值;利用该算法对齿轮的正常、齿面磨损、齿根裂纹和断齿等状态进行模式识别。实验结果验证了该算法的有效性和实用性。
Quantum Fourier transform is the basis of quantum algorithm, and is also the key of exponential efficiency. A feature extraction algorithm based on quantum Fourier transform was proposed. In this algorithm, the operation circuit of quantum computation was built. The feature extraction steps for the execution of quantum Fourier transform were configured. The estimation function of peak was constructed for evalue the extracted features. The proposed algorithm was applied to pattern recognition of gear fault conditions. The experiment results verify the efficiency and practicability of the proposed algorithm.
出处
《机床与液压》
北大核心
2015年第11期188-190,共3页
Machine Tool & Hydraulics
基金
国家自然科学基金项目(E51305454)
关键词
量子计算
量子傅里叶变换
特征提取
齿轮
模式识别
Quantum computation
Quantum Fourier transform
Feature extraction
Gear
Pattern recognition
