| 000 | 03683nlm0a2200457 4500 | ||
|---|---|---|---|
| 001 | 663076 | ||
| 005 | 20231030041842.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\34245 | ||
| 035 | _aRU\TPU\network\34151 | ||
| 090 | _a663076 | ||
| 100 | _a20210122a2020 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aDE | ||
| 105 | _ay z 100zy | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aApplication of bionic models for situation management _fO. M. Gerget, N. A. Markova |
|
| 203 |
_aText _celectronic |
||
| 225 | 1 | _aPlenary Session | |
| 300 | _aTitle screen | ||
| 320 | _a[References: 16 tit.] | ||
| 330 | _ahe article discusses the concept of choosing the sequence of control actions in order to minimize the possibility of the system statetransition to an adverse one. For this purpose, the bionic model based on the synthesis of information approach, neural networks anda genetic algorithm is developed. The functionality of each of the model elements and their interaction are presented in this paper.Special attention is paid to neuroevolutionary interaction. At the same time, information about control actions is encapsulated in thegene, which allowed increasing the functionality of the algorithm due to multidimensional data representation. The article describesthe principle of data representation in bionic models, which differs from the existing ones by the possibility of explicit or implicitrepresentation of the control action in the chromosome. In the explicit representation one neural network is formed, it describes theeffect of any of the control actions involved in the training. An implicit view creates a set of models, each of which describes the effectof only one control action. A brief description of the software implemented in the Python programming language is provided. | ||
| 461 |
_tCEUR Workshop Proceedings _oOnline Proceedings for Scientific Conferences and Workshops |
||
| 463 |
_tVol. 2763 : Computing in Physics and Technology 2020 (CPT2020) _oProceedings of the 8th International Scientific Conference, Moscow region, Russia, November 09-13, 2020 _v[5 p.] _d2020 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _ainformation approach | |
| 610 | 1 | _aneural networks | |
| 610 | 1 | _agenetic algorithm | |
| 610 | 1 | _abionic model | |
| 610 | 1 | _achoice of control actions | |
| 610 | 1 | _aнейронные сети | |
| 610 | 1 | _aбионические методы | |
| 700 | 1 |
_aGerget _bO. M. _cSpecialist in the field of informatics and computer technology _cAssociate Professor of Tomsk Polytechnic University, Candidate of technical sciences _f1974- _gOlga Mikhailovna _2stltpush _3(RuTPU)RU\TPU\pers\31430 |
|
| 701 | 1 |
_aMarkova _bN. A. _clinguist _cLecturer of Tomsk Polytechnic University _f1976- _gNatalia Aleksandrovna _2stltpush _3(RuTPU)RU\TPU\pers\32853 |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение автоматизации и робототехники _h7952 _2stltpush _3(RuTPU)RU\TPU\col\23553 |
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bШкола базовой инженерной подготовки _bОтделение иностранных языков _h8029 _2stltpush _3(RuTPU)RU\TPU\col\23510 |
| 801 | 2 |
_aRU _b63413507 _c20210122 _gRCR |
|
| 856 | 4 | _uhttp://ceur-ws.org/Vol-2763/CPT2020_paper_p-2.pdf | |
| 942 | _cCF | ||