Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control
| dc.contributor.editor | Bakhtadze, Natalia | |
| dc.contributor.editor | Yadykin, Igor | |
| dc.contributor.editor | Torgashov, Andrei | |
| dc.contributor.editor | Korgin, Nikolay | |
| dc.date.accessioned | 2023-08-08T15:27:12Z | |
| dc.date.available | 2023-08-08T15:27:12Z | |
| dc.date.issued | 2023 | |
| dc.identifier | ONIX_20230808_9783036580609_60 | |
| dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/112554 | |
| dc.description.abstract | The Special Issue aimed to bring together scientists working in various branches of control theory to discuss manufacturing control problems that include the following: enterprise control and digital ecosystem creation; the development of identification theory and methodology, and related mathematical problems; parameter, nonparametric, and structure identification and expert analysis; problems regarding selection and data analysis; control systems with an identifier; modeling in intelligent systems; simulation procedures and software; digital identification; reinforcement learning; quantum modeling; intelligent model predictive control; predictive cognitive issues; problems with software quality for complex systems; and global network resources for support processes of modeling and control. | |
| dc.language | English | |
| dc.subject.classification | thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general | en_US |
| dc.subject.classification | thema EDItEUR::P Mathematics and Science | en_US |
| dc.subject.other | decision-making | |
| dc.subject.other | psychic and behavioral components of activity | |
| dc.subject.other | action | |
| dc.subject.other | result of activity | |
| dc.subject.other | equilibrium stability | |
| dc.subject.other | consensus | |
| dc.subject.other | threshold behavior | |
| dc.subject.other | cognitive dissonance | |
| dc.subject.other | conformity | |
| dc.subject.other | informational control | |
| dc.subject.other | informational confrontation | |
| dc.subject.other | soft sensing | |
| dc.subject.other | multivariate filter | |
| dc.subject.other | reactive distillation | |
| dc.subject.other | optimal stochastic control | |
| dc.subject.other | path planning | |
| dc.subject.other | 2D random search | |
| dc.subject.other | interception | |
| dc.subject.other | external disturbances | |
| dc.subject.other | invariance | |
| dc.subject.other | block control principle | |
| dc.subject.other | decomposition | |
| dc.subject.other | high-gain factors | |
| dc.subject.other | sliding mode control | |
| dc.subject.other | sigmoid function | |
| dc.subject.other | Gramian method | |
| dc.subject.other | bilinear system process identification | |
| dc.subject.other | generalized Lyapunov equation | |
| dc.subject.other | knowledgebase | |
| dc.subject.other | associative search models | |
| dc.subject.other | wavelet analysis | |
| dc.subject.other | adaptive differential evolution | |
| dc.subject.other | evolutionary computing | |
| dc.subject.other | Hammerstein | |
| dc.subject.other | nonlinear system identification | |
| dc.subject.other | bilinear systems | |
| dc.subject.other | eigenmode decomposition | |
| dc.subject.other | spectral expansions | |
| dc.subject.other | Gramians | |
| dc.subject.other | observability | |
| dc.subject.other | controllability | |
| dc.subject.other | small-signal analysis | |
| dc.subject.other | numerical algorithm | |
| dc.subject.other | tokamak | |
| dc.subject.other | plasma equilibrium reconstruction | |
| dc.subject.other | linear plasma models | |
| dc.subject.other | identification | |
| dc.subject.other | state observer | |
| dc.subject.other | LMI | |
| dc.subject.other | least square technique | |
| dc.subject.other | deep neural network | |
| dc.subject.other | parametric uncertainty | |
| dc.subject.other | robust control | |
| dc.subject.other | super-stability | |
| dc.subject.other | regular form | |
| dc.subject.other | dynamic mode decomposition | |
| dc.subject.other | system identification | |
| dc.subject.other | Runge–Kutta method | |
| dc.subject.other | nonparametric model | |
| dc.subject.other | artificial neural network | |
| dc.subject.other | Izhikevich artificial neuron | |
| dc.subject.other | vestibular–ocular reflex | |
| dc.subject.other | control Lyapunov function | |
| dc.subject.other | Bayes criterion | |
| dc.subject.other | Haar wavelets | |
| dc.subject.other | loss function | |
| dc.subject.other | mean risk | |
| dc.subject.other | observable stochastic systems (OStS) | |
| dc.subject.other | stochastic process (StP) | |
| dc.subject.other | wavelet canonical expansion (WLCE) | |
| dc.subject.other | nonparametric identification | |
| dc.subject.other | dynamic system | |
| dc.subject.other | integral model | |
| dc.subject.other | Volterra equations | |
| dc.subject.other | smoothing cubic splines | |
| dc.subject.other | selection of the smoothing option | |
| dc.subject.other | modeling | |
| dc.subject.other | regularization | |
| dc.subject.other | inverse problems | |
| dc.subject.other | balanced identification | |
| dc.subject.other | error analysis | |
| dc.subject.other | one-dimensional heat equation | |
| dc.subject.other | n/a | |
| dc.title | Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control | |
| dc.type | book | |
| oapen.identifier.doi | 10.3390/books978-3-0365-8061-6 | |
| oapen.relation.isPublishedBy | 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 | |
| oapen.relation.isbn | 9783036580609 | |
| oapen.relation.isbn | 9783036580616 | |
| oapen.pages | 260 | |
| oapen.place.publication | Basel |
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