Temat: Kohonen networks - Katalog OPAC zbiorów

Skocz do pozycji: 1.

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

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Skocz do pozycji: 2.

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

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Skocz do pozycji: 3.

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

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