Measuring Order Parameters Based on Neural Network Technologies
We show convergence of a procedure for multiple training of a neural network, when during multiple repetitions of the solution of a binary classification task based on unchanged packets of training samples the neural emulator can initially demonstrate a chaotic set of values for the feature weights,...
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| Published in: | Measurement techniques Vol. 58; no. 4; pp. 462 - 466 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01-07-2015
Springer Nature B.V |
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| Abstract | We show convergence of a procedure for multiple training of a neural network, when during multiple repetitions of the solution of a binary classification task based on unchanged packets of training samples the neural emulator can initially demonstrate a chaotic set of values for the feature weights, which as the number of iterations increases is reduced to a certain ranked series. |
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| AbstractList | We show convergence of a procedure for multiple training of a neural network, when during multiple repetitions of the solution of a binary classification task based on unchanged packets of training samples the neural emulator can initially demonstrate a chaotic set of values for the feature weights, which as the number of iterations increases is reduced to a certain ranked series. |
| Author | Vokhmina, Yu. V. Eskov, V. M. Gavrilenko, T. V. Filatova, O. E. |
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| Cites_doi | 10.1038/nature11129 10.1007/978-3-642-79570-1 10.4236/jbise.2012.510075 |
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| Keywords | biosystem theory of chaos and self-organization neural network order parameters |
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| References | V. M. Eskov, T. V. Gavrilenko, V. V. Kozlova, and M. A. Filatov, “Measurement of the dynamic parameters of microchaos in the behavior of real biosystems,” Metrologiya, No. 7, 39–48 (2012); Measur. Techn., 55, No. 9, 1096–1100 (2012). V. M. Eskov, S. V. Kulaev, Yu. M. Popov, and O. E. Filatova, “Computer technologies in stability measurements on stationary states in dynamic biological systems,” Izmer. Tekhn., No. 1, 59–65 (2006); Measur. Techn., 49, No. 1, 59–65 (2006). PrigogineIThe die is not castFutures. Bull. World Futur. Stud. Feder.20002541719 EskovVMEskovVVFilatovaOEFilatovMATwo types of systems and three types of paradigms in systems phyilosophy and system scienceJ. Biomed. Sci. Eng.201251060260710.4236/jbise.2012.510075 ChurchlandMMCunninghamJPKaufmanMTNeural population dynamics during reachingNature201248751562012Natur.487...51C HakenHPrinciples of Brain Functioning: A Synergetic Approach to Brain Activity1996Springer, BerlinBehavior and Cognition10.1007/978-3-642-79570-10838.68092 V. M. Eskov, V. V. Eskov, and O. E. Filatova, “Characteristic features of measurements and modeling for biosystems in phase spaces of states,” Izmer. Tekhn., No. 12, 53–57 (2010); Measur. Techn., 53, No. 12, 1404–1410 (2010). V. M. Eskov, V. V. Eskov, T. V. Gavrilenko, and M. I. Zimin, “Uncertainty in quantum mechanics and biophysics of complex systems,” Vest. Mosk. Univ. Fizika. Astron., No. 5, 41–46 (2014). H Haken (735_CR7) 1996 735_CR5 735_CR6 MM Churchland (735_CR3) 2012; 487 735_CR1 VM Eskov (735_CR2) 2012; 5 I Prigogine (735_CR8) 2000; 25 735_CR4 |
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| SubjectTerms | Analytical Chemistry Chaos theory Characterization and Evaluation of Materials Classification Convergence International Measurement Science and Instrumentation Measurement techniques Medical and Biological Measurements Medicine Nervous system Neural networks Order parameters Patients Physical Chemistry Physics Physics and Astronomy Repetition Studies Tasks Training Weight reduction |
| Title | Measuring Order Parameters Based on Neural Network Technologies |
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