基于单一小波的时变信号最优分解方法

针对常规的小波变换并不能保证对信号实现最优分解。以时频性能优良的Morlet小波为单一基函数,并扩大了基函数的参数空间,确定了满足小波容许条件的参数空间边界,提高了用其分解信号的灵活性。对实数编码遗传算法进行了改进,改进后的算法免除了独立的变异操作,提高了算法的搜索速度、收敛性和准确性。以互信息最小化为寻优的判据准则,给出了基于改进的遗传算法和改造后的Morlet小波的信号最优分解的具体步骤。对时变信号的仿真分析表明所提方法是有效和可行的。

Optimal decomposition method for time-varying signal based on single type wavelet

The main function of wavelets transform is to decompose signals,but routine wavelets transform can not assure the optimal result of signal decomposition.Then a method of optimal decomposition for time-varying signal is present based on the morlet wavelet and improved genetic algorithm.In this method,morlet wavelet is reconstructed to enlarge the parameter space by changing the scal and delay factors to be variable,and the boundary of parameter space that satisfies the admissible condition of wavelet is confirmed.The minimum mutual information is selected as criterion when decomposing signal.As the method of optimization,real code genetic algorithm is improved to increase the convergence rate,success rate and anti-premature ability.The process of decomposition based on the improved genetic algorithm and reconstructed morlet wavelet is present,and the result of analysis shows that the method is effective and feasible.