Low Cost Least Squares Optimization Method for Sub-nyquist Rate Two-Phase Holding Digital-to-Analog Conversion

2019 ◽  
Author(s):  
Boyuan Wang ◽  
Zhifeng Ma
Keyword(s):  
Least Squares ◽  
Low Cost ◽  
Optimization Method ◽  
Two Phase ◽  
Least Squares Optimization ◽  
Nyquist Rate ◽  
Digital To Analog

scholarly journals Partial Least Squares Optimization Method and Path Analysis Integration for Chinese Medicine Data

2020 ◽  
Vol 32 (10) ◽  
pp. 3463
Author(s):  
Tianci Li ◽  
Wangping Xiong ◽  
Jianqiang Du ◽  
Bin Nie ◽  
Jigen Luo ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Path Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Optimization Method ◽  
Least Squares Optimization

scholarly journals PSEUDONORMAL OPTIMIZATION METHOD AND GEODESIC EQUATION CALCULATIONS

2020 ◽  
Vol 25 (3) ◽  
pp. 5-13
Author(s):  
Amridon G. Barliani ◽  
◽  
Galina A. Nefedova ◽  
Irina V. Karnetova ◽  
◽  
...  
Keyword(s):  
Least Squares ◽  
Optimization Method ◽  
Geodesic Equation ◽  
Computational Process ◽  
Pseudoinverse Matrix ◽  
Method Of Least Squares ◽  
Mathematical Algorithm ◽  
Least Squares Optimization ◽  
Correction Equations ◽  
Sequential Formation

The purpose of this paper is a comparative analysis of the methods of least squares and pseudonormal optimization on the example of equalization and estimation of the accuracy of the first-class triangulation link. Pseudonormal optimization is radically different from the traditional method of least squares optimization, since it leads to complex and cumbersome procedures for equalizing and evaluating the accuracy of the results of processing geodesic constructions due to complex formulas. A recurrent method of sequential formation of a pseudoinverse matrix of parametric correction equations is presented, which allows avoiding the time-consuming computational process of composing and solving normal equations. A mathematical algorithm for estimating the accuracy of the equalized parameters is considered. The analysis of the equation works has shown that the pseudonormal optimization method allows calculating the values of the equalized coordinates 4 times more accurately than the least squares optimization.

scholarly journals Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5697
Author(s):  
Chang Sun ◽  
Shihong Yue ◽  
Qi Li ◽  
Huaxiang Wang
Keyword(s):  
Clustering Algorithm ◽  
Two Phase Flow ◽  
Low Cost ◽  
Fast Response ◽  
New Method ◽  
Phase Flow ◽  
Reference Distribution ◽  
Two Phase ◽  
Long Time ◽  
Solid Liquid

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.

scholarly journals Method for the identification of explicit polynomial formulae for the friction in turbulent pipe flow

1999 ◽  
Vol 1 (2) ◽  
pp. 115-126 ◽  
Author(s):  
J. W. Davidson ◽  
D. Savic ◽  
G. A. Walters
Keyword(s):  
Least Squares ◽  
Symbolic Regression ◽  
Regression Method ◽  
Turbulent Pipe Flow ◽  
Model Complexity ◽  
Improve Performance ◽  
Rule Based ◽  
Least Squares Optimization ◽  
Starting Solutions ◽  
Crossover And Mutation

The paper describes a new regression method for creating polynomial models. The method combines numerical and symbolic regression. Genetic programming finds the form of polynomial expressions, and least squares optimization finds the values for the constants in the expressions. The incorporation of least squares optimization within symbolic regression is made possible by a rule-based component that algebraically transforms expressions to equivalent forms that are suitable for least squares optimization. The paper describes new operators of crossover and mutation that improve performance, and a new method for creating starting solutions that avoids the problem of under-determined functions. An example application demonstrates the trade-off between model complexity and accuracy of a set of approximator functions created for the Colebrook–White formula.

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