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Wyszukujesz frazę "Self-Organizing Maps" wg kryterium: Temat


Wyświetlanie 1-11 z 11
Tytuł:
Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor Level
Autorzy:
Kolasa, M.
Długosz, R.
Bieliński, K.
Powiązania:
https://bibliotekanauki.pl/articles/226845.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
self-organizing maps
parallel signal processing
CMOS realization
low energy consumption
digital circuits
Opis:
A new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Kohonen self-organizing maps (SOM) realized in the CMOS 0.18žm technology is presented. Simulations carried out by means of the software model of the SOM show that even low signal resolution at the output of the TNF block of 3-6 bits (depending on input data set) does not lead to significant disturbance of the learning process of the neural network. On the other hand, the signal resolution has a dominant influence on the overall circuit complexity i.e. the chip area and the energy consumption. The proposed neighborhood mechanism is very fast. For an example neighborhood range of 15 a delay between the first and the last neighboring neuron does not exceed 20 ns. This in practice means that the adaptation process starts in all neighboring neurons almost at the same time. As a result, data rates of 10-20 MHz are achievable, independently on the number of neurons in the map. The proposed SOM dissipates the power in-between 100 mW and 1 W, depending on the number of neurons in the map. For the comparison, the same network realized on PC achieves in simulations data rates in-between 10 Hz and 1 kHz. Data rate is in this case linearly dependend on the number of neurons.
Źródło:
International Journal of Electronics and Telecommunications; 2010, 56, 4; 367-373
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cluster analysis on the example of work data of the National Power System. Part 1, Comparative study of methods and conditions
Autorzy:
Tchórzewski, Jerzy
Kania, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/1819255.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
cluster analysis
National Electric Power System
MATLAB and simulink environment
ward's algorithm
self-organizing maps
Opis:
The paper presents the results of the research on the comparative study of the methods of cluster analysis and conditions, which was carried out from the point of view of their use, on the example of data concerning the operation of the National Power System. Two algorithms were used for the clustering analysis, i.e. the Ward algorithm and the algorithm of self-organizing two-dimensional maps. Cluster analysis was preceded by a review of hierarchical and non-hierarchical methods of data analysis and a description of the prepared experiment. The obtained results were interpreted. The work consists of two parts published under the same main title with different subtitles. This part 1 presents the results of the conducted review of selected methods of cluster analysis and the research conditions resulting from the adopted data on the operation of the National Power System. Part 2 presents the cluster analysis process and selected research results and their discussion.
Źródło:
Studia Informatica : systems and information technology; 2019, 1-2(23); 25--41
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification of Different-Sized Aerosol Monitoring Data
Klasyfikacja danych monitoringowych frakcji aerozolu o różnych rozmiarach cząstek
Autorzy:
Tsakovski, S.
Simeonov, V.
Powiązania:
https://bibliotekanauki.pl/articles/388252.pdf
Data publikacji:
2011
Wydawca:
Towarzystwo Chemii i Inżynierii Ekologicznej
Tematy:
chemometria
klasyfikacja
mapy samoorganizujące się
frakcja aerozolowa
chemometrics
classification
self-organizing maps
aerosol fraction
seasonal sampling
Opis:
The present study deals with the application of self-organizing maps (SOM) of Kohonen for the classification of aerosol monitoring data sets from two sampling points (Arnoldstein and Unterloibach) located close to the border between Austria and Slovenia. The goal of the chemometric data treatment was to find some specific patterns in the classification maps for five different aerosol fractions collected in four different seasons of the year. The results obtained indicated a distinct separation of the ultrafine particles (PM 0.01–PM 0.4) from the other fractions which underlines their specific effect on human health. Seasonal separation but only between summer and winter sampling is also observed.
Przedstawiono wyniki badań monitoringowych próbek aerozolu atmosferycznego pobranych z dwóch punktów pomiarowych (Arnoldstein i Unterloibach) z pobliża granicy między Austrią i Słowenią. Dane zinterpretowano z wykorzystaniem samoorganizujących się map (SOM) Kohonena. Celem chemometrycznej interpretacji danych było znalezienie charakterystycznych struktur na mapach klasyfikacji dla pięciu różnych frakcji aerozoli, zebranych w czterech różnych porach roku. Uzyskane wyniki wskazują na wyraźne oddzielenie najdrobniejszych cząstek (PM 0,01 – PM 0,4) od innych frakcji, co wskazuje na ich specyficzne działanie na zdrowie człowieka. Obserwuje się również zmiany sezonowe, ale tylko między próbkami pobranymi latem i zimą.
Źródło:
Ecological Chemistry and Engineering. A; 2011, 18, 2; 275-288
1898-6188
2084-4530
Pojawia się w:
Ecological Chemistry and Engineering. A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Nursing logistics activities in massive services
Autorzy:
Simić, D.
Powiązania:
https://bibliotekanauki.pl/articles/333536.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
systemy klasyfikacji
samoorganizujące się mapy
nursing logistics activities
classification system
learning vector quantization
self-organizing maps
Opis:
Hybrid patient classification system in nursing logistics activities is discussed in this paper. Hybrid classification model is based on two of the most used competitive artificial neural network algorithms that use learning vector quantization models (LVQ) and self-organizing maps (SOM). In general, the history of patient classification in nursing dates back to the period of Florence Nightingale. The first and the foremost condition for providing quality nursing care, which is measured by care standards, and determined by number of hours of actual care, is the appropriate number of nurses. It is possible to discus three types of experimental results. First result type could be assessment for risk of falling measured by Mors scale and pressure sores risk measured by Braden scale. Both of them are assessed by LVQ. Hybrid LVQ-SOM model is used for second result type, which presents the time for nursing logistics activities. The third type is possibility to predict appropriate number of nurses for providing quality nursing care. This research was conducted on patients from Institute of Neurology, Clinical Centre of Vojvodina.
Źródło:
Journal of Medical Informatics & Technologies; 2011, 18; 77-84
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
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:
Image Retrieval Based on Text and Visual Content Using Neural Networks
Autorzy:
Castro, D. A.
Seijas, L. M.
Powiązania:
https://bibliotekanauki.pl/articles/108732.pdf
Data publikacji:
2010
Wydawca:
Społeczna Akademia Nauk w Łodzi
Tematy:
image retrieval
Self-Organizing Maps (SOM)
content-based image retrieval (CBIR)
Text-Based Image Retrieval (TBIR)
ParBSOM
Scoring function
Opis:
In the last few years there has been a dramatic increase in the amount of visual data to be searched and retrieved. Typically, images are described by their textual content (TBIR) or by their visual features (CBIR). However, these approaches still present many problems. The hybrid approach was recently introduced, combining both characteristics to improve the benefits of using text and visual content separately. In this work we examine the use of the Self Organizing Maps for content-based image indexing and retrieval. We propose a scoring function which eliminates irrelevant images from the results and we also introduce a SOM variant (ParBSOM) that reduces training and retrieval times. The application of these techniques to the hybrid approach improved computational results.
Źródło:
Journal of Applied Computer Science Methods; 2010, 2 No. 1; 21-39
1689-9636
Pojawia się w:
Journal of Applied Computer Science Methods
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:
Neural network models for combinatorial optimization : a survey of deterministic, stochastic and chaotic approaches
Autorzy:
Smith, K.
Potvin, J.
Kwok, T.
Powiązania:
https://bibliotekanauki.pl/articles/205943.pdf
Data publikacji:
2002
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
chaos
odwzorowanie samoporządkujące
optymalizacja kombinatoryczna
sieć Hopfielda
sieć neuronowa
combinatorial optimization
deformable templates
Hopfield networks
neural networks
self-organizing maps
Opis:
This paper serves as a tutorial on the use of neural networks for solving combinatorial optimization problems. It reviews the two main classes of neural network models : the gradient-based neural networks such as the Hopfield network, and the deformable template approaches such as the elastic net method and self organizing maps. In each class, the original model is presented, its limitations discussed, and subsequent developments and extensions are reviewed. Particular emphasis is placed on stochastic and chaotic variations on the neural network models designed to improve the optimization performance. Finally, the performance of these neural network models is compared and discussed relative to other heuristic approaches.
Źródło:
Control and Cybernetics; 2002, 31, 2; 183-216
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Probabilistic morphological modeling of hydrographic networks from satellite imagery using Self-Organizing Maps
Autorzy:
Zaremba, M.
Palenichka, R.
Powiązania:
https://bibliotekanauki.pl/articles/206609.pdf
Data publikacji:
2002
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
łańcuch Markowa
modelowanie morfologiczne
odwzorowanie samoporządkujące
satelitarne rozpoznawanie obrazów
sieć neuronowa
szkieletyzacja
Markov chains
morphological modeling
neural networks
satellite image processing
self-organizing maps
skeletonization
Opis:
Adequate and concise representation of the shape of irregular objects from satellite imagery is a challenging problem in remote sensing. The conventional methods for cartographic shape representation are usually inaccurate and will provide only a rough shape description if the description process is to be fully automated. The method for automatic cartographic description of water basins presented in this paper is based on Self-Organizing Maps (SOM) - a class of neural networks with unsupervised learning. So-called structured SOM with local shape attributes such as scale and local connections of vertices are proposed for the description of object shape. The location of each vertex of piecewise linear generating curves that represent skeletons of the objects corresponds to the position of a particular SOM unit. The proposed method makes it possible to extract the object skeletons and to reconstruct the planar shapes of sparse objects based on the topological constraints of generating lines and the estimation of local scale. A context-dependent vertex connectivity test is proposed to enhance the skeletonization process. The test is based on the Markov random chain model of vertices belonging to the same generating line and the Bayesian decision-making principle. The experimental test results using Landsat-7 images demonstrate the accuracy of the proposed approach and its potential for fully automated mapping of hydrological objects.
Źródło:
Control and Cybernetics; 2002, 31, 2; 343-369
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cluster analysis on the example of work data of the National Power System. Part 2, Research and selected results
Autorzy:
Tchórzewski, Jerzy
Kania, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/2052268.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
cluster analysis
National Power System
MATLAB
Simulink
Ward's algorithm
self-organizing two-dimensional maps
Opis:
The work is a continuation of the article under the same main title and subtitle Part 1. Comparative study of methods and conditions. This article concerns the cluster analysis, which was carried out on the example of data concerning the operation of the National Power System, namely the total generations of nCDGUs and CDGUs listed by PSE Operator. Two algorithms were used to obtain the results of the cluster analysis, i.e. the Ward algorithm and the algorithm of self-organizing twodimensional topographic maps. The obtained results were interpreted and their discussion and interpretation were conducted.
Źródło:
Studia Informatica : systems and information technology; 2020, 1-2(24); 5-23
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Music Mood Visualization Using Self-Organizing Maps
Autorzy:
Plewa, M.
Kostek, B.
Powiązania:
https://bibliotekanauki.pl/articles/176410.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
music mood
music parameterization
MER (Music Emotion Recognition)
MIR (Music Information Retrieval)
Multidimensional Scaling (MDS)
principal component analysis (PCA)
Self-Organizing Maps (SOM)
ANN (Artificial Neural Networks)
Opis:
Due to an increasing amount of music being made available in digital form in the Internet, an automatic organization of music is sought. The paper presents an approach to graphical representation of mood of songs based on Self-Organizing Maps. Parameters describing mood of music are proposed and calculated and then analyzed employing correlation with mood dimensions based on the Multidimensional Scaling. A map is created in which music excerpts with similar mood are organized next to each other on the two-dimensional display.
Źródło:
Archives of Acoustics; 2015, 40, 4; 513-525
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-11 z 11

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