|
Library
|
Your profile |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|
Software systems and computational methods
Reference:
Filatova N.N., Khaneev D.M.
Respiratory noise recognition algorithm
based on the neural class models
// Software systems and computational methods.
2012. ¹ 1.
P. 67-79.
URL: https://en.nbpublish.com/library_read_article.php?id=61564
Filatova N.N., Khaneev D.M. Respiratory noise recognition algorithm based on the neural class modelsAbstract: The article describes an algorithm for detection of respiratory noise, based on the idea of growing pyramidal network adapted to operate with fuzzy descriptions of objects in the learning samples set and enriched with the linguistic interpreter for the processing results. The article contains a general functional diagram and detailed description of the individual stages of work. To describe the symptom space and the interpretation of results authors used the theory of fuzzy sets. The functioning of the algorithm is carried out in two modes: training and recognition. Neural classes models, contained in the constructed network, are interpreted in fuzzy statements, which are then used in the learning mode and provide a set of production rules for the algorithm of fuzzy logical inference. The given algorithm has a software realization, the article presents the results of testing its software implementation. Keywords: Software, classiÞ cation, recog nition, graphs, fuzzy logic, breath sounds, auscultation, respiratory sounds, model, algorithm
This article can be downloaded freely in PDF format for reading. Download article
This article written in Russian. You can find original text of the article here . References
1. Geppe, N.A. Bronkhofonografiya v kompleksnoy diagnostike bronkhial'noy astmy u detey / N.A. Geppe, V.S. Malyshev, M.N. Lisitsin i dr. // Pul'monologiya, 2002. ¹ 5, S. 33-39.
2. Kulakov, Yu.V. Vozmozhnosti kombinirovannoy bronkhofonografii v diagnostike pnevmoniy / Yu.V. Kulakov, I.Yu. Malyshenko, V.I. Korenbaum //Pul'monologiya, 2002. ¹ 5, S. 29-32.
3. Pasterkamp, H Nomenclature used by health care professionals to describe breath sounds in asthma./ H Pasterkamp, M Montgomery and W Wiebicke // Chest, 1987;92;346-352
4. Pasterkamp, H Spectral analysis of breath sounds in normal newborn infants / H Pasterkamp, R Fenton, F Leahy, V Chernick // Med Instrument, 1983; 17:355-57
5. Filatova, N.N. Neyrosetevoy algoritm i modeli nechetkoy logiki dlya zadachi klassifikatsii / N.N. Filatova, A.V. Spiridonov //Programmnye produkty i sistemy, ¹ 4, 2008
6. Grossberg, S., Competitive learning: From interactive activation to adaptive resonance // Cognitive Science, 1987, 11, 23-63
7. Gladun, V.P. Partnerstvo s komp'yuterom – Kiev: Port-Royal, 2000. – S.17-44.
8. Pospelov, D.A. Situatsionnoe modelirovanie. Teoriya i praktika – M.: Nauka, 1986. – S.174-182.
9. Kiseleva, N.N. Komp'yuternoe konstruirovanie neorganicheskikh soedineniy, perspektivnykh dlya primeneniya v elektronike, s ispol'zovaniem baz dannykh i metodov iskusstvennogo intellekta // Avtoreferat dissertatsii na soiskanie uchenoy stepeni k.t.n., Moskva, 2004.
10. Al'-Nazhzhar, N. K. Modeli, algoritmy i tekhnicheskie sredstva issledovaniya i avtomaticheskogo analiza dykhatel'nykh shumov // Avtoreferat dissertatsii na soiskanie uchenoy stepeni k.t.n., S.-Peterburg. 2007
11. Ustroystvo registratsii i analiz dykhatel'nykh shumov pat. 66174 Ros. Federatsiya: MPK A61V 5/08/ Filatova N.N., Al'-Nazhzhar N. zayavitel' i patentoobladatel' GOUFPO Tverskoy gos. tekhn. u-nt. ¹2007113453/22; zayavl. 10.04.2010; opubl. 10.09.2010
|
© 1998 – 2024 Nota Bene. Publishing Technologies. NB-Media Ltd.
Eng
