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In the Markov-based approach, the sampling resolution is chosen adaptively depending on the fitness of the last sample(s).
To facilitate the development of the LP approach, the sampling interval is properly chosen such that the discrete-time frequency is ω=π/2.
By utilizing the input delay approach, the sampling period is transformed into a time-varying yet bounded delay.
In our approach, the sampling period is much larger than the achieved accuracy, while in time-domain-based estimation, the sampling period must be smaller than the required accuracy.
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Thus, using this snowballing approach, the sample size continuously expanded, as compared with other sampling techniques.
Following this approach, the sample size increased like a rolling snowball.
This expression has no approximation when contacts approach the sample borders.
For each approach, the sample is built from the MBFE characteristics over a predefined group of functions.
Subsequent to the developed instructional approach, the samples could create 7 pieces of model representation in one semester.
On the basis of the input-delay approach, the sampled-data system is reformulated to a continuous time-varying delay system.
By applying the input delay approach, the sampled-data system is transformed into a continuous time-delay system with stochastic parameter.
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