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    Wyświetlanie 1-8 z 8
    Tytuł:
    Soft Fault Clustering in Analog Electronic Circuits with the Use of Self Organizing Neural Network
    Autorzy:
    Grzechca, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/220571.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    fault detection
    parametric faults
    analogue electronic circuits
    self-organizing neural network
    Opis:
    The paper presents a methodology for parametric fault clustering in analog electronic circuits with the use of a self-organizing artificial neural network. The method proposed here allows fast and efficient circuit diagnosis on the basis of time and/or frequency response which may lead to higher production yield. A self-organizing map (SOM) has been applied in order to cluster all circuit states into possible separate groups. So, it works as a feature selector and classifier. SOM can be fed by raw data (data comes from the time or frequency response) or some pre-processing is done at first. The author proposes conversion of a circuit response with the use of e.g. gradient and differentiation. The main goal of the SOM is to distribute all single faults on a two-dimensional map without state overlapping. The method is aimed for the development stage because the tolerances of elements are not taken into account, however single but parametric faults are considered. Efficiency analyses of fault clustering have been made on several examples e.g. a Sallen-Key BPF and an ECG amplifier. Testing procedure is performed in time and frequency domains for the Sallen-Key BPF with limited number of test points i.e. it is assumed that only input and output pins are available. A similar procedure has been applied to a real ECG amplifier in the frequency domain. Results prove a high efficiency in acceptable time which makes the method very convenient (easy and quick) as a first test in the development stage.
    Źródło:
    Metrology and Measurement Systems; 2011, 18, 4; 555-568
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wykorzystanie sieci neuronowej Kohonena do wizualizacji danych MPG
    Use of Kohonen neural network in MPG data visualisation
    Autorzy:
    Oszutowska-Mazurek, D. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/135818.pdf
    Data publikacji:
    2016
    Wydawca:
    Wyższa Szkoła Techniczno-Ekonomiczna w Szczecinie
    Tematy:
    sieć neuronowa Kohonena
    samoorganizujące się mapy
    SOM
    wizualizacja danych
    dane MPG
    Kohonen neural network
    self organizing map
    Opis:
    Wstęp i cel: Zastosowanie sieci neuronowych Kohonena zapewnia zmniejszenie wielowymiarowości danych. Wizualizacja w postaci map samoorganizujących się (SOM) jest użytecznym narzędziem do wstępnego kastrowania (grupowania) danych. Materiał i metody: Wizualizację przeprowadzona dla rzeczywistych danych, udostępnionych przez uniwersytet w Kalifornii za pomocą oprogramowania SNNS v.4.3. Głównym celem pracy jest zastosowanie sieci neuronowych Kohonena zapewniające zmniejszenie wielowymiarowości danych. Wyniki: Otrzymano wizualizacje danych wskazujące jednoznacznie na dodatnie i ujemne korelacje danych MPG. Wniosek: Mapy samoorganizujące się mogą być dedykowane wizualizacji danych wielowymiarowych jednak wyniki zależą od sposobu mapowania danych wejściowych, zwłaszcza o charakterze jakościowym, nawet jeśli stosowana jest normalizacja każdego z parametrów.
    Introduction and aim: The use of Kohonen neural network ensures the decrease of data multidimensionality. Visualisation called Self organized maps is useful tool for preliminary data clustering. Material and methods: The visualisation of real data set was obtained with the use of program SNNS v.4.3 for real dataset from California University. The main aim of this paper is the use of Kohonen neural network to ensure the reduction of multidimensional data. Results: Obtained visualisations of data indicate unambiguously positive and negative correlations for MPG data Conclusion: Self organising maps could be dedicated to multidimensional data visualisation and preliminary quality assessment, but the results depend on the mapping method of input data, especially quantity type, even if normalisation of every parameter is provided.
    Źródło:
    Problemy Nauk Stosowanych; 2016, 4; 19-30
    2300-6110
    Pojawia się w:
    Problemy Nauk Stosowanych
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Pattern layer reduction for a generalized regression neural network by using a self-organizing map
    Autorzy:
    Kartal, S.
    Oral, M.
    Ozyildirim, B. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/329728.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    generalized regression neural network
    artificial neural network
    self organizing map
    nearest neighbour
    reduced dataset
    sztuczna sieć neuronowa
    mapa samoorganizująca
    metoda najbliższych sąsiadów
    redukcja zbioru danych
    Opis:
    In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of training samples in the dataset. The use of a GRNN in applications that have relatively large datasets becomes troublesome due to the architecture and speed required. The great number of neurons in the pattern layer requires a substantial increase in memory usage and causes a substantial decrease in calculation speed. Therefore, there is a strong need for pattern layer size reduction. In this study, a self-organizing map (SOM) structure is introduced as a pre-processor for the GRNN. First, an SOM is generated for the training dataset. Second, each training record is labelled with the most similar map unit. Lastly, when a new test record is applied to the network, the most similar map units are detected, and the training data that have the same labels as the detected units are fed into the network instead of the entire training dataset. This scheme enables a considerable reduction in the pattern layer size. The proposed hybrid model was evaluated by using fifteen benchmark test functions and eight different UCI datasets. According to the simulation results, the proposed model significantly simplifies the GRNN’s structure without any performance loss.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 2; 411-424
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A connectionist computational method for face recognition
    Autorzy:
    Pujol, F. A.
    Mora, H.
    Girona-Selva, J. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/330558.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    pattern recognition
    face recognition
    neural network
    self organizing map
    rozpoznawanie wzorca
    rozpoznawanie twarzy
    sieć neuronowa
    samodzielne organizowanie map
    Opis:
    In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 2; 451-465
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Sieci Kohonena jako narzędzie wspomagające budowę prognoz kombinowanych
    Self-Organizing maps as a tool supporting the construction of combined forecasts
    Autorzy:
    Perzyńska, Joanna
    Powiązania:
    https://bibliotekanauki.pl/articles/449693.pdf
    Data publikacji:
    2017
    Wydawca:
    Zachodniopomorska Szkoła Biznesu w Szczecinie
    Tematy:
    modele ekonometryczne
    prognozy kombinowane
    sztuczne sieci neuronowe
    sieci Kohonena
    artificial neural network
    combined forecasts
    econometric models
    Self-Organizing Maps
    Opis:
    W artykule przedstawiono propozycję wykorzystania sieci Kohonena we wstępnym etapie budowy prognoz kombinowanych. Przy pomocy sieci Kohonena można podzielić zbiór dostępnych modeli na rozłączne klasy, a następnie dokonać redukcji ich liczby. Prognozy składowe prognoz kombinowanych wyznacza się wówczas na podstawie modeli należących do różnych klas, co ma zapewnić niepowielanie informacji oraz zwiększyć dokładność prognoz kombinowanych. Ilustracją rozważań o charakterze teoretycznym jest przykład empiryczny, w którym prognozy (indywidualne i kombinowane) wyznaczono dla zmiennej mikroekonomicznej wykazującej wahania sezonowe. Przeprowadzone badania potwierdziły użyteczność zaproponowanej metody.
    In the paper, the author presents suggestion of application of Self-Organizing Maps in the preliminary stage of construction of combined forecasts. Using the SOM, the set of available models can be divided into disjoint classes and then reduced. The component forecasts are then determined on the basis of models belonging to different classes, to ensure that information is not duplicated and to increase the accuracy of the combined forecasts. The illustration of theoretical considerations is the empirical example, in which individual and combined forecasts are calculated for economic variable with seasonal fluctations. The research confirms the usefulness of the suggested method.
    Źródło:
    Zeszyty Naukowe ZPSB Firma i Rynek; 2017, 2(52); 77-85
    2657-3245
    Pojawia się w:
    Zeszyty Naukowe ZPSB Firma i Rynek
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multiscale evaluation of a thin-bed reservoir
    Autorzy:
    Lis-Śledziona, Anita
    Powiązania:
    https://bibliotekanauki.pl/articles/1841759.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    thin beds
    high resolution well logs prediction
    horizontal resistivity
    unsupervised neural network
    self-organizing maps (SOM)
    electrofacies
    low resistivity pay
    Opis:
    A thin-bed laminated shaly-sand reservoir of the Miocene formation was evaluated using two methods: high resolution microresistivity data from the XRMI tool and conventional well logs. Based on high resolution data, the Earth model of the reservoir was defined in a way that allowed the analyzed interval to be subdivided into thin layers of sandstones, mudstones, and claystones. Theoretical logs of gamma ray, bulk density, horizontal and vertical resistivity were calculated based on the forward modeling method to describe the petrophysical properties of individual beds and calculate the clay volume, porosity, and water saturation. The relationships amongst the contents of minerals were established based on the XRD data from the neighboring wells; hence, the high-resolution lithological model was evaluated. Predicted curves and estimated volumes of minerals were used as an input in multimineral solver and based on the assumed petrophysical model the input data were recalculated, reconstructed and compared with the predicted curves. The volumes of minerals and input curves were adjusted during several runs to minimalize the error between predicted and recalculated variables. Another approach was based on electrofacies modeling using unsupervised self-organizing maps. As an input, conventional well logs were used. Then, the evaluated facies model was used during forward modeling of the effective porosity, horizontal resistivity and water saturation. The obtained results were compared and, finally, the effective thickness of the reservoir was established based on the results from the two methods.
    Źródło:
    Geology, Geophysics and Environment; 2021, 47, 1; 5-20
    2299-8004
    2353-0790
    Pojawia się w:
    Geology, Geophysics and Environment
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine learning-based analysis of English lateral allophones
    Autorzy:
    Piotrowska, Magdalena
    Korvel, Gražina
    Kostek, Bożena
    Ciszewski, Tomasz
    Czyżewski, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/908115.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    allophone
    audio features
    artificial neural network
    k-nearest neighbor
    self organizing map
    alofon
    cechy akustyczne
    sztuczna sieć neuronowa
    metoda najbliższych sąsiadów
    mapa samoorganizująca
    Opis:
    Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 393-405
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A class of neuro-computational methods for assamese fricative classification
    Autorzy:
    Patgiri, C.
    Sarma, M.
    Sarma, K. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/91763.pdf
    Data publikacji:
    2015
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    neuro-computational classifier
    fricative phonemes
    Assamese language
    Recurrent Neural Network
    RNN
    neuro fuzzy classifier
    linear prediction cepstral coefficients
    LPCC
    self-organizing map
    SOM
    adaptive neuro-fuzzy inference system
    ANFIS
    klasyfikator neuronowy
    klasyfikator neuronowo rozmyty
    sieć Kohonena
    Opis:
    In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 1; 59-70
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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