Parameter preference for the continuous super-twisting-like algorithm based on H∞ norm analysis
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DOI: 10.1016/j.ifacol.2020.12.1157
Abstract: In variable structured systems, plenty of designs are built to be homogeneous. Such unperturbed homogeneous dynamics with negative homogeneous degree guarantee finite time convergence. Previous studies provide lower bounds for parameters that result in such… read more here.
Keywords: twisting like; parameter preference; continuous super; norm analysis ... See more keywords
Baseline correction based on L1-Norm optimization and its verification by a computer vision method
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DOI: 10.1016/j.soildyn.2020.106047
Abstract: Abstract Baseline correction in time series of ground motion is a challenging problem in research pertaining to strong ground motion. In previous studies in this area, numerous methods have been proposed to identify baseline drift.… read more here.
Keywords: baseline correction; norm optimization; method; baseline ... See more keywords
Age-based norm-reference values for the Pediatric Eating Assessment Tool
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DOI: 10.1038/s41390-018-0067-z
Abstract: Background and objectivesDifferentiating problematic feeding from variations of typical behavior is a challenge for pediatric providers. The Pediatric Eating Assessment Tool (PediEAT) is a parent-report measure of symptoms of problematic feeding in children 6 months… read more here.
Keywords: pediatric eating; age; assessment tool; age based ... See more keywords
Robust canonical correlation analysis based on L1-norm minimization for feature learning and image recognition
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DOI: 10.1117/1.jei.29.2.023001
Abstract: Abstract. Canonical correlation analysis (CCA) is a popular method that has been extensively used in feature learning. In nature, the objective function of CCA is equivalent to minimizing the distance of the paired data, and… read more here.
Keywords: correlation analysis; norm minimization; feature learning; feature ... See more keywords
A Multiple Kernel Learning Model Based on p-Norm
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DOI: 10.1155/2018/1018789
Abstract: By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a… read more here.
Keywords: mkl; based gmkl; norm constraint; norms based ... See more keywords