Modified gradient algorithm for total least square filtering

2006 ◽  
Vol 70 (1-3) ◽  
pp. 568-576 ◽  
Author(s):  
Xiangyu Kong ◽  
Chongzhao Han ◽  
Ruixuan Wei
Keyword(s):  
Least Square ◽  
Gradient Algorithm ◽  
Total Least Square

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

scholarly journals A Sort of New Improved Algorithm For Total Least Square

2015 ◽  
Vol 9 (1) ◽  
pp. 238-247
Author(s):  
Deng Yonghe
Keyword(s):  
Least Square ◽  
New Method ◽  
Unknown Parameters ◽  
Error Equation ◽  
Virtual Observation ◽  
Condition Equation ◽  
Total Least Square ◽  
Improved Algorithm

Aim to blemish of total least square algorithm based on error equation of virtual observation, this paper put forward and deduced a sort of new improved algorithm which selects essential unknown parameters among designing matrix, and then, doesn’t consider condition equation of unknown parameters among designing matrix. So, this paper perfected and enriched algorithm, and sometimes, new method of this paper is better. Finally, the results of examples showed that new mothod is viable and valid.

Penerbitan Maklumat Tiga Dimensi Sebuah Lukisan Garisan Menggunakan Kaedah Jumlah Kuasa Dua Terkecil

2012 ◽  
Author(s):  
Habibollah Haron ◽  
Dzulkifli Mohamed
Keyword(s):  
Linear System ◽  
Line Drawing ◽  
Least Square Method ◽  
Least Square ◽  
Geometric Information ◽  
Ordinary Least Square ◽  
Total Least Square ◽  
Regular Line ◽  
Space Image

Pengekstrakan lukisan garisan melibatkan proses menukar lukisan garisan tidak sekata kepada lukisan sekata, mendapatkan entiti asas simpang, garisan serta kawasan dan seterusnya mendapatkan maklumat tiga dimensi lukisan. Proses penukaran lukisan tidak sekata akan menghasilkan maklumat dua dimensi lukisan dan maklumat geometri dua dimensi lukisan iaitu simpang, garisan dan kawasan masing-masing yang mewakili unjuran bucu, pinggir dan permukaan dalam tiga dimensi. Berdasarkan maklumat geometri dua dimensi ini, jenis kenalaran imej ditentukan untuk membentuk set sistem linear lebih tentu. Grimstead menggunakan empat kenalaran imej dan menggunakan lelaran kuasa dua terkecil untuk menyelesaikan sistem linear lebih tentu yang dihasilkan. Kertas kerja ini akan mencadangkan kaedah jumlah kuasa dua terkecil untuk menyelesaikan sistem linear lebih tentu yang dibentuk oleh dua kenalaran imej. Perbandingan dengan kaedah Grimstead akan ditunjukkan dan penerangan akan dibantu oleh kes kajian dan paparan output. Kata kunci: Terjemahan garisan; Jumlah Kuasa Dua Terkecil; ruang gradien; kenalaran imej; sistem linear Line drawing interpretation involves process of converting irregular line drawing to regular line drawing. The converting process produces junctions, lines and regions that are two-dimensional projection of vertices, edges, and faces of a solid model respectively. Based on the geometric information obtained, image regularities are determined and a over-determined sets of linear systems is developed. Grimstead used the three image regularities in the linear system and iterative ordinary least square to solve them. The paper is intended to propose Total Least Square method in solving over-determined sets of linear system of image regularities of a line drawing. Two image regularities have been used. The solutions obtained are visualized with the help of MATLAB tool. Case study is given to assist the explaination. Key words: Line Interpretation; Total Least Square Method; Gradient Space; Image Regularities; Linear System

REAL-TIME BLIND SOURCE SEPARATION OF ACOUSTIC SIGNALS WITH A RECURSIVE APPROACH

2004 ◽  
Vol 04 (02) ◽  
pp. 193-206 ◽  
Author(s):  
SHUXUE DING ◽  
JIE HUANG ◽  
DAMING WEI
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Correlation Matrix ◽  
Lower Cost ◽  
Source Separation ◽  
Least Square ◽  
Gradient Algorithm ◽  
Normal Equation ◽  
Recursive Least Square ◽  
Second Order Statistics

We propose an approach for real-time blind source separation (BSS), in which the observations are linear convolutive mixtures of statistically independent acoustic sources. A recursive least square (RLS)-like strategy is devised for real-time BSS processing. A normal equation is further introduced as an expression between the separation matrix and the correlation matrix of observations. We recursively estimate the correlation matrix and explicitly, rather than stochastically, solve the normal equation to obtain the separation matrix. As an example of application, the approach has been applied to a BSS problem where the separation criterion is based on the second-order statistics and the non-stationarity of signals in the frequency domain. In this way, we realise a novel BSS algorithm, called exponentially weighted recursive BSS algorithm. The simulation and experimental results showed an improved separation and a superior convergence rate of the proposed algorithm over that of the gradient algorithm. Moreover, this algorithm can converge to a much lower cost value than that of the gradient algorithm.

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