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Wyszukujesz frazę "least squares support vector machine" wg kryterium: Temat


Wyświetlanie 1-5 z 5
Tytuł:
A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM
Autorzy:
Zhang, C.
He, Y.
Zuo, L.
Wang, J.
He, W.
Powiązania:
https://bibliotekanauki.pl/articles/221378.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
analog circuits
incipient fault diagnosis
wavelet transform
kernel entropy component analysis
least squares support vector machine
Opis:
Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.
Źródło:
Metrology and Measurement Systems; 2015, 22, 2; 251-262
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of Kaplan turbine coordination tests based on least squares support vector machine with an improved grey wolf optimization algorithm
Autorzy:
Kong, Fannie
Xia, Jiahui
Yang, Daliang
Luo, Ming
Powiązania:
https://bibliotekanauki.pl/articles/2173627.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Kaplan turbine
coordination tests
least squares support vector machine
improved grey wolf optimization
turbina Kaplana
test koordynacyjny
metoda najmniejszych kwadratów
ulepszona optymalizacja szarego wilka
Opis:
The optimum combination of blade angle of the runner and guide vane opening with Kaplan turbine can improve the hydroelectric generating the set operation efficiency and the suppression capability of oscillations. Due to time and cost limitations and the complex operation mechanism of the Kaplan turbine, the coordination test data is insufficient, making it challenging to obtain the whole curves at each head under the optimum coordination operation by field tests. The field test data is employed to propose a least-squares support vector machine (LSSVM)-based prediction model for Kaplan turbine coordination tests. Considering the small sample characteristics of the test data of Kaplan turbine coordination, the LSSVM parameters are optimized by an improved grey wolf optimization (IGWO) algorithm with mixed non-linear factors and static weights. The grey wolf optimization (GWO) algorithm has some deficiencies, such as the linear convergence factor, which inaccurately simulates the actual situation, and updating the position indeterminately reflects the absolute leadership of the leader wolf. The IGWO algorithm is employed to overcome the aforementioned problems. The prediction model is simulated to verify the effectiveness of the proposed IGWO-LSSVM. The results show high accuracy with small samples, a 2.59% relative error in coordination tests, and less than 1.85% relative error in non-coordination tests under different heads.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137124
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of Kaplan turbine coordination tests based on least squares support vector machine with an improved grey wolf optimization algorithm
Autorzy:
Kong, Fannie
Xia, Jiahui
Yang, Daliang
Luo, Ming
Powiązania:
https://bibliotekanauki.pl/articles/2128160.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Kaplan turbine
coordination tests
least squares support vector machine
improved grey wolf optimization
turbina Kaplana
test koordynacyjny
metoda najmniejszych kwadratów
ulepszona optymalizacja szarego wilka
Opis:
The optimum combination of blade angle of the runner and guide vane opening with Kaplan turbine can improve the hydroelectric generating the set operation efficiency and the suppression capability of oscillations. Due to time and cost limitations and the complex operation mechanism of the Kaplan turbine, the coordination test data is insufficient, making it challenging to obtain the whole curves at each head under the optimum coordination operation by field tests. The field test data is employed to propose a least-squares support vector machine (LSSVM)-based prediction model for Kaplan turbine coordination tests. Considering the small sample characteristics of the test data of Kaplan turbine coordination, the LSSVM parameters are optimized by an improved grey wolf optimization (IGWO) algorithm with mixed non-linear factors and static weights. The grey wolf optimization (GWO) algorithm has some deficiencies, such as the linear convergence factor, which inaccurately simulates the actual situation, and updating the position indeterminately reflects the absolute leadership of the leader wolf. The IGWO algorithm is employed to overcome the aforementioned problems. The prediction model is simulated to verify the effectiveness of the proposed IGWO-LSSVM. The results show high accuracy with small samples, a 2.59% relative error in coordination tests, and less than 1.85% relative error in non-coordination tests under different heads.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137124, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efficiency of linear and non-linear classifiers for gas identification from electrocatalytic gas sensor
Autorzy:
Kalinowski, P.
Woźniak, Ł.
Strzelczyk, A.
Jasinski, P.
Jasinski, G.
Powiązania:
https://bibliotekanauki.pl/articles/221796.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electrocatalytic sensor
cyclic voltammetry
data pre-processing
support vector machine (SVM)
Partial Least Squares Discriminant Analysis
Opis:
Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling.
Źródło:
Metrology and Measurement Systems; 2013, 20, 3; 501-512
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of the influence of mining impacts on the intensity of damage to masonry building structures
Analiza wpływu oddziaływań górniczych na intensywność uszkodzeń budynków murowanych
Autorzy:
Firek, K.
Powiązania:
https://bibliotekanauki.pl/articles/105166.pdf
Data publikacji:
2017
Wydawca:
Politechnika Rzeszowska im. Ignacego Łukasiewicza. Oficyna Wydawnicza
Tematy:
technical condition
buildings
masonry structure
mining impacts
Partial Least Squares Regression
multiple regression analysis
Support Vector Machine
stan techniczny
budynek
konstrukcja murowana
wpływy górnicze
metoda cząstkowych najmniejszych kwadratów
PLSR
analiza regresji wielorakiej
metoda wektorów podpierających
SVM
Opis:
The paper presents the results of the analysis of the extent of damage to building structures subjected to mining impacts in the form of tremors and continuous surface deformation. The two methods which were used included the multiple regression analysis and the Support Vector Machine – SVM, which belongs to the socalled Machine Learning. The study used the database of the design, technical condition and potential causes of damage to 199 non-renovated buildings, up to the age of 20 years, of a traditional brick construction, located in the mining area of Legnica-Głogów Copper District (LGOM). The conducted analysis allowed for the qualitative assessment of the influence of mining impacts on the extent of damage to the studied buildings.
W referacie przedstawiono wyniki analizy zakresu uszkodzeń budynków poddanych oddziaływaniom górniczym w postaci wstrząsów oraz ciągłych deformacji terenu. Posłużono się statystyczną metodą regresji wielorakiej oraz metodą wektorów podpierających (Support Vector Machine – SVM) zaliczaną do tzw. uczenia maszynowego (Machine Learning). W badaniach wykorzystano bazę danych o konstrukcji, stanie technicznym i potencjalnych przyczynach uszkodzeń 199 nieremontowanych budynków w wieku do 20 lat, o tradycyjnej konstrukcji murowanej, usytuowanych na terenie górniczym Legnicko-Głogowskiego Okręgu Miedziowego (LGOM). Przeprowadzona analiza pozwoliła na jakościową ocenę wpływu oddziaływań górniczych na zakres uszkodzeń badanych budynków.
Źródło:
Czasopismo Inżynierii Lądowej, Środowiska i Architektury; 2017, 64, 1; 69-79
2300-5130
2300-8903
Pojawia się w:
Czasopismo Inżynierii Lądowej, Środowiska i Architektury
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-5 z 5

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