Sentence examples for order estimation and from inspiring English sources

Order estimation and spurious mode elimination concerning the subspace identification technique are discussed in this paper.

The routine proposes a set of optimum experimental settings to support structural model definition, kinetic order estimation and parameter estimation during a model building procedure and process characterization.

In addition, a joint SNR and sparsity order estimation, and the conditions for implementing the compressed sensing, are suggested to give consideration to the practical application of the proposed scheme.

This mechanism has been proven to be a simple and reliable way of order estimation and the same principle has been applied to detect the number of emitter sources in array signal processing [4], to determine the number of principal components in signal and image analysis [5, 6] and to estimate the system order in blind system identification [7, 8].

It has previously been demonstrated that the optimal filtering methods perform extremely well with respect to fundamental frequency estimation under adverse conditions, and this fact, combined with the new results on model order estimation and efficient implementation, suggests that these methods form an appealing alternative to classical methods for analyzing multi-pitch signals.

Nonlinear modeling, universal methods, and order estimation are advanced topics that are also considered.

Overall, it can be concluded that the optimal filtering methods form an intriguing alternative for joint fundamental frequency and order estimation, especially so for multi-pitch signals.

Recently, a pitch estimation filter with amplitude compression (PEFAC) in the spectral domain has been proposed in [17], and methods for joint pitch and model order estimation have been proposed in [18] [20].

To take into account the applicability of the scheme, we give a possible solution of joint SNR and sparsity order estimation in this section.

It is tested both on synthetic and fMRI data, and is compared to two traditional techniques for model order estimation.

Model order estimation is especially difficult for ill-conditioned systems.