Performance analysis of the least squares based LTI channel identification algorithm using random matrix methods

2011 ◽  
Author(s):  
Milutin Pajovic ◽  
James C. Preisig
Keyword(s):  
Performance Analysis ◽  
Least Squares ◽  
Random Matrix ◽  
Identification Algorithm ◽  
Channel Identification ◽  
Matrix Methods

scholarly journals Identification of an unstable ARMA equation

2001 ◽  
Vol 7 (1) ◽  
pp. 97-112 ◽  
Author(s):  
Yulia R. Gel ◽  
Vladimir N. Fomin
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Infinite Order ◽  
Time Series Model ◽  
Identification Algorithm ◽  
Data Handling ◽  
Stochastic Time Series ◽  
Stochastic Time ◽  
Strongly Consistent ◽  
Minimal Phase

Usually the coefficients in a stochastic time series model are partially or entirely unknown when the realization of the time series is observed. Sometimes the unknown coefficients can be estimated from the realization with the required accuracy. That will eventually allow optimizing the data handling of the stochastic time series.Here it is shown that the recurrent least-squares (LS) procedure provides strongly consistent estimates for a linear autoregressive (AR) equation of infinite order obtained from a minimal phase regressive (ARMA) equation. The LS identification algorithm is accomplished by the Padé approximation used for the estimation of the unknown ARMA parameters.

scholarly journals Pengaruh Modal Insani dan Modal Sosial terhadap Kinerja (Studi Kasus Usaha Kecil dan Menengah (UKM) Makanan dan Minuman Kota Bogor)

2016 ◽  
Vol 5 (3) ◽  
pp. 244
Author(s):  
Layla Khoirrini ◽  
Lindawati Kartika
Keyword(s):  
Performance Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Equation Modelling

<p>Usaha Kecil dan Menengah (UKM) makanan dan minuman kota Bogor mampu menyerap banyak tenaga kerja. Tenaga kerja merupakan aset bagi perusahaan dalam bentuk modal insani dan modal sosial. Tujuan penelitian ini adalah menganalisis secara deskriptif, menganalisis pengaruh modal insani dan modal sosial terhadap kinerja UKM makanan dan minuman Kota Bogor, serta memformulasikan rekomendasi untuk meningkatkan kinerja UKM. Metode penelitian yang digunakan antara lain analisis deskriptif, <em>Importance Performance Analysis </em>(IPA), <em>fishbone</em> diagram, dan <em>Structural Equation Modelling</em> (SEM) dengan pendekatan <em>Partial Least Squares </em>(PLS). Hasil analisis SEM menyatakan bahwa pengetahuan lain  dan dimensi struktural berpengaruh terhadap modal insani dan modal sosial, di mana modal insani dan modal sosial berpengaruh positif dan signifikan terhadap kinerja. Oleh karena itu, peningkatan kinerja pada UKM direkomendasikan melalui beberapa kegiatan penunjang antara lain: membentuk sistem pengendalian mutu dan penyusunan standar pelaksanaan produksi, memperbaiki sarana prasarana yang dimiliki oleh UKM, serta ikut serta dalam pelatihan untuk meningkatkan kompetensi pekerja UKM.</p>Kata kunci: kinerja UKM, modal insani, modal sosial, <em>partial least squares, </em><em>structural equation modelling</em>

scholarly journals Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1137 ◽  
Author(s):  
Haoyuan Sha ◽  
Fei Mei ◽  
Chenyu Zhang ◽  
Yi Pan ◽  
Jianyong Zheng
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Least Squares ◽  
Short Circuit ◽  
Singular Value ◽  
Support Vector ◽  
Voltage Sag ◽  
Svm Classifier ◽  
Identification Algorithm ◽  
Value Decomposition

Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for solving the problem and ensuring the safe grid operation. Therefore, with the measured data uploaded by the sag monitoring system, this paper proposes a sag type identification algorithm based on K-means-Singular Value Decomposition (K-SVD) and Least Squares Support Vector Machine (LS-SVM). Firstly; each phase of the sag sample RMS data is sparsely coded by the K-SVD algorithm and the sparse coding information of each phase data is used as the feature matrix of the sag sample. Then the LS-SVM classifier is used to identify the sag type. This method not only works without any dependence on the sag data feature extraction by artificial ways, but can also judge the short-circuit fault phase, providing more effective information for the repair of grid faults. Finally, based on a comparison with existing methods, the accuracy advantages of the proposed algorithm with be presented.

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