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Linear dynamical systems under random trains of non-overlapping, arbitrary-shape pulses: generalized approach

机译:非重叠,任意形状脉冲的随机序列下的线性动力系统:广义方法

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摘要

The excitation considered is a train of non-overlapping pulses, defined as a train of alternate random pulse durations and time gaps between the consecutive pulses. The time gaps and durations are assumed to be independent, continuous random variables. In this model the pulses are not only non-overlapping, but they are also non-adjoining, i.e. they can only adjoin with probability zero. All durations are assumed to be characterized by one common, Erlang (or integer parameter gamma) type, probability distribution. Likewise, the time gaps are all identically, Erlang distributed, but the probability distributions of the durations and of the time gaps have different parameters. Pulse shapes are given by an arbitrary, deterministic function and pulse heights are given by independent, identically distributed random variables. The mean value and autocorrelation function of the pulse train are evaluated analytically and are shown to be expressed in terms of the renewal densities of the renewal process governing the pulse arrivals. It is shown that for rectangular pulses with equal, deterministic heights these expressions reduce to the ones obtained independently with the aid of another approach, where the mean value and autocorrelation function are given in terms of Markov 'on' state probabilities. The mean value and variance of the response of a linear oscillator are obtained via a time domain analysis. Illustrative numerical analysis is carried out for example trains of pulses with parabolic and rectangular pulse shapes.
机译:所考虑的激励是一系列不重叠的脉冲,定义为一系列交替的随机脉冲持续时间和连续脉冲之间的时间间隔。假设时间间隔和持续时间是独立的,连续的随机变量。在该模型中,脉冲不仅不重叠,而且不相邻,即它们只能以零概率相邻。假定所有持续时间的特征在于一种常见的Erlang(或整数参数gamma)类型的概率分布。同样,时间间隔全部相同,为Erlang分布,但持续时间和时间间隔的概率分布具有不同的参数。脉冲形状由任意确定性函数给出,脉冲高度由独立的,均匀分布的随机变量给出。通过分析评估了脉冲序列的平均值和自相关函数,并显示为表示控制脉冲到达的更新过程的更新密度。结果表明,对于具有相同确定高度的矩形脉冲,这些表达式可以简化为借助另一种方法独立获得的表达式,其中均值和自相关函数以马尔可夫“接通”状态概率给出。线性振荡器的响应的平均值和方差是通过时域分析获得的。例如,执行具有抛物线形和矩形脉冲形状的脉冲序列的说明性数值分析。

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