Determination of model order and structure

2011 ◽  
pp. 123-148
Keyword(s):  
Model Order

scholarly journals Multivariate Distributions in Hydrology and River Regulation

1976 ◽  
Vol 7 (5) ◽  
pp. 265-280
Author(s):  
N.A. Kartvelishvili ◽  
L.T. Gottschalk
Keyword(s):  
Markov Process ◽  
Finite Difference ◽  
Difference Scheme ◽  
River Runoff ◽  
Finite Difference Scheme ◽  
River Regulation ◽  
Distribution Functions ◽  
Multivariate Distributions ◽  
Model Order

It is assumed that the river runoff process can be approximated by a Markov process. The process is thus described by M distribution functions: Fn (qt, t ; qt-1; t-1;…;qt-n, t-n), t ≡ 1, 2, …, M where M is the number of time intervals within the year, n - the order of the Markov process and qt, in general, is a vector representing runoff at several sites in a river or neighbouring rivers. Fundamental hypothesis of relations between multivariate distributions and corresponding marginal distributions is given. A finite difference scheme for multisite and multilag generation of river runoff is derived. The derivation is based on the multivariate normal distribution. Different methods for determination of the order of the finite difference scheme are discussed as well as the influence of model order and method of parameter estimation on properties of the model.

scholarly journals A Modification of Gustavsen and Semlyen’s vector fitting algorithm for overestimated model order

2021 ◽  
Author(s):  
Andrew Mackay
Keyword(s):  
Control Parameter ◽  
Infinite Order ◽  
Null Space ◽  
Original Function ◽  
Model Order ◽  
Vector Fitting ◽  
Fitting Algorithm ◽  
Improve Accuracy ◽  
Space Method

Gustavsen and Semlyen’s vector fitting algorithm (VFA) is popular for the determination of the poles and residues of a sampled response function, modeled as a finite order rational function. However, it does not always perform well when the original function is of infinite order and/or the model order is overestimated. This article is concerned with a novel “null-space” modification of the VFA that provides an accurate representation of the original function when the model order is large and when the original VFA performs poorly. The null-space method is parameterized by a single control parameter, L, which can be adjusted to improve accuracy. Comparisons are made with the original VFA and another of Gustavsen’s modifications

Determination of model order for NARX models directly from input-output data

1998 ◽  
Vol 8 (5-6) ◽  
pp. 459-468 ◽  
Author(s):  
John D. Bomberger ◽  
Dale E. Seborg
Keyword(s):  
Input Output ◽  
Output Data ◽  
Model Order ◽  
Narx Models

Online Dynamic Compensation of Pressure Sensor

2015 ◽  
Vol 775 ◽  
pp. 420-425
Author(s):  
Jun Xiang Zhang ◽  
Kun Shan Ge ◽  
Zhan Bao Gao ◽  
Shao Peng Dong
Keyword(s):  
Frequency Band ◽  
Pressure Sensor ◽  
Experimental Verification ◽  
Dynamic Process ◽  
Dynamic Compensation ◽  
Model Order ◽  
Digital Compensation ◽  
Identification Method ◽  
Simultaneous Identification

An online dynamic compensation of the pressure sensor based on simultaneous identification method of model order and parameter is proposed to identify the model of pressure sensor and set digital compensation links for the pressure sensor. While simulating, the compensation links can broaden the frequency band and improve the dynamic process of the pressure sensor. This article describes the realization of the digital dynamic compensation with DSP processor and the process of the experimental verification. The results show that the simultaneous identification method can be used in determination of model and digital compensation links for pressure sensor effectively. And DSP measurement device can complete online dynamic compensation effectively.

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