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Wyszukujesz frazę "Kohonen networks" wg kryterium: Temat


Wyświetlanie 1-9 z 9
Tytuł:
Kohonen networks as ships classifier
Autorzy:
Żak, A.
Powiązania:
https://bibliotekanauki.pl/articles/331686.pdf
Data publikacji:
2008
Wydawca:
Polskie Towarzystwo Akustyczne
Tematy:
Kohonen networks
hydroacoustics
ships classifier
Opis:
The paper presents the technique of artifficial neural networks used as classifier of hydroacoustic signatures generated by moving ship. In the paper firstly the method of feature extraction from hydracoustic signatures using calculation of Mel-Frequency Cepstral Coefficients was discussed. Next the mathod of feature matching using for purpose of object classification basing on hydroacoustic signatures was described. The technique of artificial neural networks especially Kohonen networks which belongs to group of self organizing networks where chosen to solve the research problem of classification. the choice was caused by some advantages of mentioned kind of neural networks for example they are ideal for finding relationships amongst complex sets of data, they have possibility to self expand the set answers for new input vectors. To check the correctness of classifier work the research in which the number of right classification for presented and not presented before hydroacoustic signatures were made. some results of research were presented on this paper.
Źródło:
Hydroacoustics; 2008, 11; 467-476
1642-1817
Pojawia się w:
Hydroacoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The application of Kohonen networks for identification of leaders in the trade sector in Czechia
Autorzy:
Vrbka, Jaromír
Nica, Elvira
Podhorská, Ivana
Powiązania:
https://bibliotekanauki.pl/articles/22446390.pdf
Data publikacji:
2019
Wydawca:
Instytut Badań Gospodarczych
Tematy:
trade sector
Kohonen networks
leaders in the field
cluster analysis
return on equity
Opis:
Research background: The trade sector is considered to be the power of economy, in developing countries in particular. With regard to the Czech Republic, this field of the national economy constitutes the second most significant employer and, at the same time, the second most significant contributor to GNP. Apart from traditional methods of business analyzing and identifying leaders, artificial neural networks are widely used. These networks have become more popular in the field of economy, although their potential has yet to be fully exploited. Purpose of the article: The aim of this article is to analyze the trade sector in the Czech Republic using Kohonen networks and to identify the leaders in this field. Methods: The data set consists of complete financial statements of 11,604 enterprises that engaged in trade activities in the Czech Republic in 2016. The data set is subjected to cluster analysis using Kohonen networks. Individual clusters are subjected to the analysis of absolute indicators and return on equity which, apart from other, shows a special attraction of individual clusters to potential investors. Average and absolute quantities of individual clusters are also analyzed, which means that the most successful clusters of enterprises in the trade sector are indicated. Findings & Value added: The results show that a relatively small group of enter-prises enormously influences the development of the trade sector, including the whole economy. The results of analyzing 319 enterprises showed that it is possible to predict the future development of the trade sector. Nevertheless, it is also evident that the trade sector did not go well in 2016, which means that investments of owners are minimal.
Źródło:
Equilibrium. Quarterly Journal of Economics and Economic Policy; 2019, 14, 4; 739-761
1689-765X
2353-3293
Pojawia się w:
Equilibrium. Quarterly Journal of Economics and Economic Policy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Use of neural networks in risk assessment and optimization of insurance cover in innovative enterprises
Autorzy:
Pukała, R.
Powiązania:
https://bibliotekanauki.pl/articles/398804.pdf
Data publikacji:
2016
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
innovative enterprise
risk
neural networks
Kohonen networks
business insurance
przedsiębiorstwo innowacyjne
ryzyko
sieci neuronowe
sieci Kohonena
ubezpieczenia gospodarcze
Opis:
The scientific objective of the paper is to present the findings of a study into the use of artificial neural networks in quantifying activity related risks of an innovative enterprise and to optimize its insurance cover in order to minimize the probable financial losses whenever they materialize. The Kohonen network involving the activation of 51 input variables was applied in the study. The outcomes of the stimulation for the given set of variables made it possible to determine the probability of a threat occurring in the classes. The results of the analysis were used to prepare an optimal insurance cover for the activities of the innovative company. The research findings are suitable for use in risk theory as well as in issues relating to entrepreneurship and insurance. The analytical device employed can also be put to practical use as a support tool in corporate risk management.
Źródło:
Ekonomia i Zarządzanie; 2016, 8, 3; 43-56
2080-9646
Pojawia się w:
Ekonomia i Zarządzanie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pulse shape discrimination of neutrons and gamma rays using kohonen artificial neural networks
Autorzy:
Tambouratzis, T.
Chernikova, D.
Pzsit, I.
Powiązania:
https://bibliotekanauki.pl/articles/91759.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
shape
neutron
discrimination
gamma rays
Kohonen artificial neural networks
ANNs
linear vector quantisation
LVQ
self-organizing map
SOM
pulse shape discrimination
PSD
Opis:
The potential of two Kohonen artificial neural networks (ANNs) - linear vector quantisation (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n’s) and gamma rays (’s). The effect that (a) the energy level, and (b) the relative size of the training and test sets, have on identification accuracy is also evaluated on the given PSD dataset. The two Kohonen ANNs demonstrate complementary discrimination ability on the training and test sets: while the LVQ is consistently more accurate on classifying the training set, the SOM exhibits higher n/ identification rates when classifying new patterns regardless of the proportion of training and test set patterns at the different energy levels; the average time for decision making equals ˜100 μs in the case of the LVQ and ˜450 μs in the case of the SOM.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 2; 77-88
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The method of neuron weight vector initial values selection in Kohonen network
Autorzy:
Chandzlik, S.
Powiązania:
https://bibliotekanauki.pl/articles/333164.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
neural networks
Kohonen network
neurological diseases diagnosis
Parkinson disease
hemiparesis after ischemic stroke
Opis:
Diagnosing of morbid conditions by means of automatic tools supported by computers is a significant and often used element in modern medicine. Some examples of these tools are automatic conclusion-making units of Parotec System for Windows (PSW). In the initial period of PSW system implementation, the units were used for recognition of orthopaedic diseases on the basis of the patient's walk and posture [15,17]. Subsequently, many additional options have been implemented, which have been used for purposes of diagnosing neurological diseases [1,2,3,9,12]. During automatic classification of diseases the additional units use elements of neural networks. The vectors based on normalised diagnostic measures [3] are inputs of the units. The measurements describe a patient's posture condition, his walk and overloads occurring on his feet. The Counter-Propagation (CP), two-layer network has been used in one of the automatic conclusion-making units. During CP network activity, we can see not only supervised but unsupervised learning processes as well. This is a characteristic feature of the CP network. The initial steps of the CP network learning process are very important, because the success of the network training process depends on them to a great extent. Therefore, a new method of weight vector initial values selection was proposed. The efficiency of the method was compared with classical methods. The results were very satisfactory. Owing to the proposed method, the time of the network training process as well as the mean-square error and the classification error was reduced. The research has been carried out using clinical cases of some neurological diseases: Parkinson's Disease, left-lateral hemiparesis and right-lateral hemiparesis after ischemic stroke. The measurements, which were made on a control group of patients without any neurological diseases, were the reference for these diagnostic classes.
Źródło:
Journal of Medical Informatics & Technologies; 2006, 10; 189-197
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sensor Actor Network Modeling utilizing the Holonic Architectural Framework
Autorzy:
Chiu, C.
Chaczko, Z.
Moses, P.
Powiązania:
https://bibliotekanauki.pl/articles/226170.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Extended Kohonen Maps (EKM)
Sensor Actor Networks (SANET)
Wireless Sensor Networks (WSN)
SANET Middleware
POE Classification Model
holonic architecture
Opis:
This paper discusses the results of utilizing advanced EKM modeling techniques to manage Sensor-Actor networks (SANETs) based upon the Holonic Architectural Framework. EKMs allow a quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize from a given input space to administer SANET routing and clustering functions with a control parameter space. Results demonstrate that in comparison to linear approximation techniques, indirect mapping with EKMs provide fluid control and feedback mechanisms by operating in a continuous sensory control space – thus enabling interactive detection and optimization of events in real-time environments.
Źródło:
International Journal of Electronics and Telecommunications; 2010, 56, 1; 49-54
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deployment of an Agent-based SANET Architecture for Healthcare Services
Autorzy:
Chiu, C.
Chaczko, Z.
Powiązania:
https://bibliotekanauki.pl/articles/226290.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
BDI Agent Framework
Extended Kohonen Maps (EKM)
healthcare infrastructures
Rao-Blackwell filtering
Sensor Actuator Networks (SANETs)
Opis:
This paper describes the adaptation of a computational technique utilizing Extended Kohonen Maps (EKMs) and Rao-Blackwell-Kolmogorov (R-B) Filtering mechanisms for the administration of Sensor-Actuator networks (SANETs). Inspired by the BDI (Belief-Desire-Intention) Agent model from Rao and Georgeff, EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize, while the Rao-Blackwell filtering mechanism reduces the external noise and interference in the problem set introduced through the self-organization process. Initial results demonstrate that a combinatorial approach to optimization with EKMs and Rao-Blackwell filtering provides an improvement in event trajectory approximation in comparison to standalone cooperative EKM processes to allow responsive event detection and optimization in patient healthcare.
Źródło:
International Journal of Electronics and Telecommunications; 2011, 57, 3; 309-315
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Speech nonfluency detection and classification based on linear prediction coefficients and neural networks
Autorzy:
Kobus, A.
Kuniszyk-Jóźkowiak, W.
Smołka, E.
Codello, I.
Powiązania:
https://bibliotekanauki.pl/articles/333600.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
przewidywanie liniowe
liniowe kodowanie predykcyjne
sieci nuronowe
kowariancja
brak płynności
mowa
wykrywanie
perceptron
linear prediction
LPC
neural networks
Kohonen
covariance
nonfluency
speech
detection
radial
Opis:
The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying.
Źródło:
Journal of Medical Informatics & Technologies; 2010, 15; 135-143
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of selected methods of multi-parameter data visualization used for classification of coals
Autorzy:
Jamroz, D.
Niedoba, T.
Powiązania:
https://bibliotekanauki.pl/articles/110329.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
multidimensional visualization
observational tunnels method
multidimensional scaling
MDS
principal component analysis
PCA
relevance maps
autoassociative neural networks
Kohonen maps
parallel coordinates method
grained material
coal
Opis:
Methods of multi-parameter data visualization through the transformation of multidimensional space into two-dimensional one allow to present multidimensional data on computer screen, thus making it possible to conduct a qualitative analysis of this data in the most natural way for human – by a sense of sight. In the paper a comparison was made to show the efficiency of selected seven methods of multidimensional visualization and further, to analyze data describing various coal type samples. Each of the methods was verified by checking how precisely a coal type can be classified when a given method is applied. For this purpose, a special criterion was designed to allow an evaluation of the results obtained by means of each of these methods. Detailed information included presentation of methods, elaborated algorithms, accepted parameters for best results as well the results. The framework for the comparison of the analyzed multi-parameter visualization methods includes: observational tunnels method multidimensional scaling MDS, principal component analysis PCA, relevance maps, autoassociative neural networks, Kohonen maps and parallel coordinates method.
Źródło:
Physicochemical Problems of Mineral Processing; 2015, 51, 2; 769-784
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-9 z 9

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