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


Wyświetlanie 1-2 z 2
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:
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
    Wyświetlanie 1-2 z 2

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