This paper considers the problem of optimally approximating high-order transfer functions by low-order ones with a time delay.
In this article, we propose a new design method of an FIR digital filter that optimally approximates a given analog filter in the sense of minimizing the H∞ norm of the sampled-data error system.
The subjects of this paper are descent algorithms to optimally approximate a strictly convex contour with a polygon.
Hence, given a set of L points, their quantization in the diffusion space allows to optimally approximate the meta-states of the latent Markov system [57], in terms of diffusion distance distortion.
We show that mechanisms based on the student-proposing deferred acceptance may fail to satisfy those objectives, but that by using instead the school-proposing deferred acceptance together with a choice function used by the schools, which incorporates a preference for satisfying them, can optimally approximate the diversity objectives while still satisfying an appropriate fairness criterion.
In this work, we establish the saddle-point variational formulation for a two-sided time-dependent fractional diffusion problem over (H^{1}(Omega times H^{-{betaover 2}}(Omega times L^{2}(Omega)) and develop its fully discrete expanded mixed finite element procedure, which approximates optimally the unknown function u, its derivative q and the fractional flux p.
As can be seen from the Fig. 5, the samples being selected optimally can approximate to the actual position, and the results with weighted average calculation make the average localization error as small as possible.
Hence, we have that the problem in (32) can be approximated as a GP which is efficiently and optimally solvable by convex optimization software[49].
When wavelet non-linear functions are used in the neurons of a neural network we can estimate the wavelet parameters scale and translation and the relative importance (weight) of each basis function optimally with respect to approximating a given function (signal) in the minimum mean square error sense or with respect to classifying a function (signal) with minimum mean classification error.
It is actually approximated at at $227,750.
