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中文摘要：
变步长随机共振算法有效解决了绝热近似大参数条件下的弱信号检测问题.基于信号近似熵测度的自适应随机共振,实现了变步长随机共振最优输出的自适应求解.周期信号的近似熵不受其幅值和相位变化的影响,而只与其频率及信噪比有关.因此,按照原始数据的采样条件,构造待检测频率在预定信噪比下的标准信号,并以其近似熵为基准,通过自动调节非线性系统的结构参数和计算步长,求得系统输出的近似熵距离矩阵.该矩阵中的最小值所对应的即为自适应条件下非线性动力系统的最优参数.
英文摘要：
Weak signal detection under the condition of adiabatic elimination in large parameters has been solved by step-changed stochastic resonance algorithm. Adaptive stochastic resonance based on approximate entropy measurement is proposed, and it can give the best result of the step-changed stochastic resonance adaptively. Because the approximate entropy of the periodic signal does not suffer from the change of its amplitude and phase, a periodic signal of frequency f0 with given signal-to-noise ratio which is to be detected can be made under the same condition as the raw data, and its approximate entropy is calculated as the criterion. By adjusting the structural parameters and calculation step automatically, a series output of the bistable system can be got, and an approximate entropy distance matrix can be constructed. After getting the minimum value of the matrix, the best parameters of the nonlinear dynamical system can be obtained.
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