Optimization routine Sample Clauses

Optimization routine. The procedure described here involves the application of a deconvolution fitting error minimization routine (DMR) in step one and two. The parameterized UR model (URp, Eq. 2.3) and the parameterized CDLD model (CDLDp, Eq. 2.4) are used to predict a recorded eCAP waveform using indirect deconvolution. This indirect procedure uses a convolution step to estimate URh and CDLD by implementing DMR to optimize the match between eCAPp and the recorded eCAP. The initial values and boundary limits of the URp and CDLDp parameters must be assigned for the DMR to run. Then, the eCAPp and the baseline-corrected eCAP (eCAPc) (see details in the next section), initial values and boundary limits of the URp and CDLDp parameters are used as input into the DMR. The DMR iteratively manipulates the parameters of the URp and the CDLDp in step one (Fig. 2.2A), or only the CDLDp in step two (Fig. 2.2B) within the boundary limits to minimize the fitting error (see Fig. 2.2) with the lsqcurvefit function provided in MATLAB (Mathworks 2016a, Natick, MA, USA). The fitting error refers to the difference between the eCAPp and the eCAPc. Accordingly, the eCAPp gradually converges to the eCAPc. When the fitting error reaches the minimum value (i.e., the eCAPp optimally approximates the eCAPc), the DMR outputs the values of the URp and CDLDp parameters. With these values, the UR and CDLD (step one) and subsequently the final CDLD estimate (step two) can be generated. The MATLAB script of this DMR is attached in the supplementary material of this publication.
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