改进投票策略的Morlet小波核支持向量机及应用

Morlet Wavelet Kernel SVM Improved by Voting Strategy and Its Application

主要研究了现有支持向量机存在的问题,提出基于贝叶斯优化投票策略和Morlet小波作为核函数的改进方法。通过贝叶斯优化改进支持向量机分类投票策略,实现对不可分区域数据的有效分类。通过建立Morlet小波核支持向量机,使向量机更加适合冲击非线性信号的分类,并用一个滚动轴承的实例说明方法的鲁棒性和可靠性。

Aiming at the existing problems of support vector machine(SVM),a new method based on Bayesian optimization and the establishment of the Morlet wavelet kernel SVM is proposed.The Bayesian optimization is used to improve the voting strategy of SVM.The Morlet wavelet kernel SVM is established to make it more appropriate for the classification of impact and nonlinear signals.An example of a rolling bearing proves that the proposed method is robustness and reliable.