2016
DOI: 10.1007/978-3-319-28397-5_17
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Adaptive Input Shaping for Flexible Systems Using an Extreme Learning Machine Algorithm Identification

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Cited by 1 publication
(2 citation statements)
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“…Among these pulses, there are only D nonzero pulses, denoted as A = {A 1 , A 2 , • • • , A D } and t = {t 1 , t 2 , • • • , t D }. Hence, the discrete expression of H can be expressed as (19):…”
Section: Adaptive Adjustment Of Shaper Parametersmentioning
confidence: 99%
See 1 more Smart Citation
“…Among these pulses, there are only D nonzero pulses, denoted as A = {A 1 , A 2 , • • • , A D } and t = {t 1 , t 2 , • • • , t D }. Hence, the discrete expression of H can be expressed as (19):…”
Section: Adaptive Adjustment Of Shaper Parametersmentioning
confidence: 99%
“…In [18], the pulse amplitude and time of a ZV shaper were iteratively updated in real time according to the constructed performance index and the measured output and control data. Moreover, an adaptive input-shaping method based on an extreme learning machine (ELM) was proposed in [19]. Specifically, the pulse response sequence of the closed-loop system was identified and fitted using an online frequency ELM algorithm, and the shaper parameters were updated in real time.…”
Section: Introductionmentioning
confidence: 99%