Autor: Osowski, S. - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: MLP and SVM classifiers for fault detection
Klasyfikatory neuronowe MLP i SVM dla potrzeb diagnostyki
Autorzy:: Osowski, S.
Tematy:: klasyfikatory neuronowe
perceptron wielowarstwowy
MLP
sieć wektorów podtrzymujących
SVM
sztuczna inteligencja
diagnostyka
diagnostic
neural classifier
electrical circuits; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/258282.pdf Link otwiera się w nowym oknie
Opis:: The paper presents a comparative analysis of two of the most important neural network classifiers: the multilayer perceptron (MLP) and Support Vector Machine (SVM) in application to diagnostic problems. The structure as well as learning algorithms of both networks have been presented and compared. The results of numerical experiments comparing the performance of both classifiers on the artificial and real life problems are presented and discussed.
Praca przedstawia dwa rozwiązania klasyfikatorów neuronowych na potrzeby diagnostyki. Jednym z nich jest perceptron wielowarstwowy (ang. MultiLayer Perceptron - MLP), drugim sieć wektorów podtrzymujących (ang. Support Vector Machine (SVM). Przedstawiono struktury oraz podstawowe metody uczenia takich sieci. Działania obu klasyfikatorów sprawdzono i porównano na problemach testowych, zarówno typu syntetycznego, jak i problemie rzeczywistym rozpoznawania uszkodzeń elementów w rzeczywistym układzie filtru elektrycznego.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 2.

Tytuł:: Data mining methods for gene selection on the basis of gene expression arrays
Autorzy:: Muszyński, M.
Osowski, S.
Tematy:: gene expression array
gene ranking
feature selection
clusterization measures
fusion
SVM classification
ekspresja genów
selekcja cech
klasyfikacja SVM; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/329803.pdf Link otwiera się w nowym oknie
Opis:: The paper presents data mining methods applied to gene selection for recognition of a particular type of prostate cancer on the basis of gene expression arrays. Several chosen methods of gene selection, including the Fisher method, correlation of gene with a class, application of the support vector machine and statistical hypotheses, are compared on the basis of clustering measures. The results of applying these individual selection methods are combined together to identify the most often selected genes forming the required pattern, best associated with the cancerous cases. This resulting pattern of selected gene lists is treated as the input data to the classifier, performing the task of the final recognition of the patterns. The numerical results of the recognition of prostate cancer from normal (reference) cases using the selected genes and the support vector machine confirm the good performance of the proposed gene selection approach.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 3.

Tytuł:: Data mining methods for prediction of air pollution
Autorzy:: Siwek, K.
Osowski, S.
Tematy:: computational intelligence
feature selection
neural network
random forest
air pollution forecasting
inteligencja obliczeniowa
selekcja cech
sieć neuronowa
lasy losowe
zanieczyszczenie powietrza; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/330775.pdf Link otwiera się w nowym oknie
Opis:: The paper discusses methods of data mining for prediction of air pollution. Two tasks in such a problem are important: generation and selection of the prognostic features, and the final prognostic system of the pollution for the next day. An advanced set of features, created on the basis of the atmospheric parameters, is proposed. This set is subject to analysis and selection of the most important features from the prediction point of view. Two methods of feature selection are compared. One applies a genetic algorithm (a global approach), and the other—a linear method of stepwise fit (a locally optimized approach). On the basis of such analysis, two sets of the most predictive features are selected. These sets take part in prediction of the atmospheric pollutants PM10, SO2, NO2 and O3. Two approaches to prediction are compared. In the first one, the features selected are directly applied to the random forest (RF), which forms an ensemble of decision trees. In the second case, intermediate predictors built on the basis of neural networks (the multilayer perceptron, the radial basis function and the support vector machine) are used. They create an ensemble integrated into the final prognosis. The paper shows that preselection of the most important features, cooperating with an ensemble of predictors, allows increasing the forecasting accuracy of atmospheric pollution in a significant way.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 4.

Tytuł:: Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose
Autorzy:: Osowski, S.
Siwek, K.
Tematy:: data mining
electronic nose
gasoline blends
random forest
support vector machine
wavelet denoising; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/220792.pdf Link otwiera się w nowym oknie
Opis:: The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 5.

Tytuł:: Neuro-fuzzy TSK network for approximation of static and dynamic functions
Autorzy:: Linh, T.
Osowski, S.
Tematy:: algorytm uczenia się
aproksymacja
sieć neuronowo-rozmyta
approximation
learning algorithms
neuro-fuzzy networks; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Powiązania:: https://bibliotekanauki.pl/articles/205951.pdf Link otwiera się w nowym oknie
Opis:: The paper presents the neuro-fuzzy network in application to the approximation of the static and dynamic functions. The network implements the Takagi-Sugeno inference rules. The learning algorithm is based on the hybrid approach, splitting the learning phase into two stages : the adaptation of the linear output weights using the SVD algorithm and the conventional steepest descent backpropagation rule in application to the adaptation of the nonlinear parameters of the membership functions. The new approach to the generation of the inference rules, based on the fuzzy self-organization is proposed and the algorithm of automatic determination of the number of these rules has been also implemented. The method has been applied for the off-line modelling of static nonlinear relations and on-line simulation of the dynamic systems.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 6.

Tytuł:: Bagging and boosting techniques in prediction of particulate matters
Autorzy:: Triana, D.
Osowski, S.
Tematy:: ensemble of predictors
bagging
boosting
PM pollution; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/202449.pdf Link otwiera się w nowym oknie
Opis:: The paper presents new ensemble solutions, which can forecast the average level of particulate matters PM10 and PM2.5 with increased accuracy. The proposed network is composed of weak predictors integrated into a final expert system. The members of the ensemble are built based on deep multilayer perceptron and decision tree and use bagging and boosting principle in elaborating common decisions. The numerical experiments have been carried out for prediction of daily average pollution of PM10 and PM2.5 for the next day. The results of experiments have shown, that bagging and boosting ensembles employing these weak predictors improve greatly the quality of results. The mean absolute errors have been reduced by more than 30% in the case of PM10 and 20% in the case of PM2.5 in comparison to individually acting predictors.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 7.

Tytuł:: Local dynamic integration of ensemble in prediction of time series
Autorzy:: Osowski, S.
Siwek, K.
Tematy:: neural networks
ensemble of predictors
dynamic integration
time series prediction
sieci neuronowe
zespół predyktorów
dynamiczna integracja; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/201557.pdf Link otwiera się w nowym oknie
Opis:: The paper presents local dynamic approach to integration of an ensemble of predictors. The classical fusing of many predictor results takes into account all units and takes the weighted average of the results of all units forming the ensemble. This paper proposes different approach. The prediction of time series for the next day is done here by only one member of an ensemble, which was the best in the learning stage for the input vector, closest to the input data actually applied. Thanks to such arrangement we avoid the situation in which the worst unit reduces the accuracy of the whole ensemble. This way we obtain an increased level of statistical forecasting accuracy, since each task is performed by the best suited predictor. Moreover, such arrangement of integration allows for using units of very different quality without decreasing the quality of final prediction. The numerical experiments performed for forecasting the next input, the average PM10 pollution and forecasting the 24-element vector of hourly load of the power system have confirmed the superiority of the presented approach. All quality measures of forecast have been significantly improved.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 8.

Tytuł:: Ensemble of data mining methods for gene ranking
Autorzy:: Wiliński, A.
Osowski, S.
Tematy:: gene expression array
feature selection
gene ranking methods
classification
SVM; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/201570.pdf Link otwiera się w nowym oknie
Opis:: The paper presents the ensemble of data mining methods for discovering the most important genes and gene sequences generated by the gene expression arrays, responsible for the recognition of a particular type of cancer. The analyzed methods include the correlation of the feature with a class, application of the statistical hypotheses, the Fisher measure of discrimination and application of the linear Support Vector Machine for characterization of the discrimination ability of the features. In the first step of ranking we apply each method individually, choosing the genes most often selected in the cross validation of the available data set. In the next step we combine the results of different selection methods together and once again choose the genes most frequently appearing in the selected sets. On the basis of this we form the final ranking of the genes. The most important genes form the input information delivered to the Support Vector Machine (SVM) classifier, responsible for the final recognition of tumor from non-tumor data. Different forms of checking the correctness of the proposed ranking procedure have been applied. The first one is relied on mapping the distribution of selected genes on the two-coordinate system formed by two most important principal components of the PCA transformation and applying the cluster quality measures. The other one depicts the results in the graphical form by presenting the gene expressions in the form of pixel intensity for the available data. The final confirmation of the quality of the proposed ranking method are the classification results of recognition of the cancer cases from the non-cancer (normal) ones, performed using the Gaussian kernel SVM. The results of selection of the most significant genes used by the SVM for recognition of the prostate cancer cases from normal cases have confirmed a good accuracy of results. The presented methodology is of potential use for practical application in bioinformatics.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 9.

Tytuł:: Computerised system for fault diagnosis of the rotor bars of squirrel-cage induction motor
Komputerowy system diagnostyczny uszkodzeń prętów klatki maszyny indukcyjnej
Autorzy:: Osowski, S.
Kurek, J.
Siwek, K.
Tematy:: klatkowa maszyna indukcyjna
wykrywanie uszkodzeń
pręt
maszyna wektorów nośnych
przetwarzanie sygnału
squirrel-cage induction motor
bar fault detection
support vector machine (SVM)
signal processing; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/257946.pdf Link otwiera się w nowym oknie
Opis:: The paper presents the computerised system for the diagnosis of the rotor bars of an induction electrical motor. The solution relies on the processing of the measured stator current and application of the Support Vector Machine as the classifier. The important point is the generation of the diagnostic features on the basis of which the SVM classifier undertakes its decision whether or not the bars are faulty. The most important problem is concerned with the generation of the diagnostic features, on the basis of which the recognition of the state of the rotor bars is done. In our approach, we use the spectral information of the stator current, limited to a strictly specified region. The selected features form the input vector applied to the single class Support Vector Machine, responsible for recognition of the fault. The results of the numerical experiments are presented and discussed in the paper.
Praca przedstawia skomputeryzowany automatyczny system diagnostyczny do wykrywania uszkodzeń prętów maszyny indukcyjnej. Rozwiązanie jest typu bezinwazyjnego i może być zastosowane do maszyny w ruchu. Sygnały diagnostyczne generowane są na podstawie zarejestrowanych sygnałów prądu statora. W aplikacji wykorzystano jednoklasową sieć SVM (ang. Support Vector Machine) pracującą jako klasyfikator. Jednym z najistotniejszych problemów rozwiązanych w tym zadaniu jest generacja i selekcja odpowiednich cech diagnostycznych, na podstawie których klasyfikator dokonuje rozpoznania stanu prętów. Zaproponowano cechy bazujące na charakterystyce spektralnej prądu statora, ograniczonej do wybranego zakresu częstotliwości związanego z poślizgiem maszyny. System zbudowany w ramach projektu jest w pełni zautomatyzowany, poczynając od akwizycji sygnałów, poprzez ich przetwarzanie wstępne, aż po końcowy werdykt (pręty uszkodzone bądź nieuszkodzone).
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 10.

Tytuł:: Ensemble neural network approach for accurate load forecasting in a power system
Autorzy:: Siwek, K.
Osowski, S.
Szupiluk, R.
Tematy:: sieć neuronowa
ślepa separacja sygnałów
prognozowanie obciążenia
neural network
blind source separation
ensemble of predictors
load forecasting; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/907659.pdf Link otwiera się w nowym oknie
Opis:: The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Informacja

Wyszukujesz frazę "Osowski, S." wg kryterium: Autor