稀疏分解将信号表达为冗余字典中少量原子的线性组合,其分解的精度对其广泛应用具有重要影响。 提出了基于量子进化算法的稀疏分解方法,利用增强型量子比特概率幅对Gabor原子进行染色体编码,采 用简化形式的梯度进化操作和逐代缩减的变异操作进行种群个体的更新,以稀疏分解的残余信号与Gabor原子 的内积作为适应度函数,筛选出每次稀疏分解的最佳原子。通过两个仿真信号的稀疏分解实验和轴承振动信号 的故障特征提取实验,验证了所提方法较其他方法具有更高的分解精度。

Sparse decomposition expresses a signal as a linear combination of a small number of atoms in a redundant dictionary. The accuracy of the decomposition has an important influence on its wide application. A sparse decomposition method based on quantum evolution algorithm is proposed. The Gabor atoms are encoded by the enhanced qubit probability amplitude. Simplified gradient evolution operation and generation-by-generation reduction mutation operation are used to update the individual population. And the inner products of the residual signal of sparse decomposition and Gabor atoms are used as the fitness to filter out the best atoms for each sparse decomposition. Two experiments on sparse decomposition of simulation signals and the fault feature extraction experiments of the bearing vibration signals are carried out. It’s proved that the proposed method has a higher decomposition accuracy than the other methods.