Models of neural networks with fuzzy activation functions
| Parent link: | IOP Conference Series: Materials Science and Engineering Vol. 177 : Mechanical Engineering, Automation and Control Systems (MEACS 2016).— 2017.— [012031, 5 p.] |
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| Egile nagusia: | |
| Erakunde egilea: | |
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| Gaia: | Title screen This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc. |
| Hizkuntza: | ingelesa |
| Argitaratua: |
2017
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| Saila: | Information technologies in Mechanical Engineering |
| Gaiak: | |
| Sarrera elektronikoa: | http://dx.doi.org/10.1088/1757-899X/177/1/012031 http://earchive.tpu.ru/handle/11683/37844 |
| Formatua: | Baliabide elektronikoa Liburu kapitulua |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=654029 |
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| 200 | 1 | |a Models of neural networks with fuzzy activation functions |f A. T. Nguyen, A. M. Korikov | |
| 203 | |a Text |c electronic | ||
| 225 | 1 | |a Information technologies in Mechanical Engineering | |
| 300 | |a Title screen | ||
| 320 | |a [References: 11 tit.] | ||
| 330 | |a This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc. | ||
| 461 | 0 | |0 (RuTPU)RU\TPU\network\2008 |t IOP Conference Series: Materials Science and Engineering | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\19514 |t Vol. 177 : Mechanical Engineering, Automation and Control Systems (MEACS 2016) |o International Conference, October 27–29, 2016, Tomsk, Russia |o [proceedings] |f National Research Tomsk Polytechnic University (TPU) ; eds. A. P. Zykova ; N. V. Martyushev |v [012031, 5 p.] |d 2017 | |
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| 610 | 1 | |a активация | |
| 610 | 1 | |a нечеткие нейронные сети | |
| 610 | 1 | |a импульсы | |
| 700 | 1 | |a Nguyen |b A. T. | |
| 701 | 1 | |a Korikov |b A. M. |c radiophysicist, specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University, doctor of technical sciences |f 1942- |g Anatoly Mikhailovich |2 stltpush |3 (RuTPU)RU\TPU\pers\35166 | |
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