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The complexity of the proposed methods is linear with the number of outer iterations whereas at each iteration, it mainly requires the lattice search along four and two trigonometric parameters, respectively.
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Traditional modal analysis based methods are linear methods.
First two methods are linear classifiers, whereas NN is non-linear.
While the first two methods were linear over the concentration ranges of 2.0 to 30 and 1.5 to 10 μg/ml, respectively, the third method was linear over a much wider concentration range of 40 to 180 μg/ml.
The methods were linear (>0.995) over the range tested (0.1 100 ng/g) with LOR for most pesticides at 1 ng/g or ppb.
The methods were linear in the 0.5 100 ng/mL range in plasma and urine, and 5 300 pg/mg in tissues with determination coefficients > 0.99.
The four evaluated methods were linear least squares [41], linear discriminate analysis [42], a backpropagation (BP) neural network [43], and the support vector machine with either the linear (LISVM) or the radial-basis function kernels (RBFSVM) [44].
The methods were linear over a concentration range from 10 to 500 ng/ml, the inter-assay precision expressed as coefficient of variation was <10%.
In regards to computational complexity, these methods are linear in the number of image pixels.
The methods were linear (r > 0.99) over the concentration ranges studied.
In addition, these methods are linear; thus, they are not sufficient for dealing with complex models, especially those in which there are nonlinear interactions between parameters.
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