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| 001 | 395473 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230315133318.0 | ||
| 006 | a||||fo|||| 00| 0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 211025s2022 sz | s |||| 0|eng d | ||
| 020 | _a9783030883966 | ||
| 024 | 7 |
_a10.1007/978-3-030-88396-6 _2doi |
|
| 040 |
_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC |
||
| 050 | 4 |
_aQA402.5 _b2022 EB |
|
| 100 | 1 |
_aRafajłowicz, Wojciech _eautor _9687363 |
|
| 245 | 1 | 0 |
_aLearning Decision Sequences For Repetitive Processes-Selected Algorithms _cby Wojciech Rafajłowicz |
| 250 | _a1st edition 2022 | ||
| 264 | 1 |
_aCham _bSpringer International Publishing _c2022 |
|
| 300 |
_a1 recurso en línea (XI, 126 páginas) _b32 ilustraciones, 19 ilustraciones a color |
||
| 336 |
_atexto _btxt _2rdacontent |
||
| 337 |
_aelectrónico _bc _2rdamedia |
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| 338 |
_arecurso electrónico _bcr _2rdacarrier |
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| 347 |
_aarchivo de texto _bPDF |
||
| 490 | 0 |
_aStudies in Systems Decision and Control _x2198-4190 _v401 |
|
| 505 | 0 | _aIntroduction -- Basic notions and notations -- Learning decision sequences -- Differential evolution with a population filter -- Decision making for COVID-19 suppression -- Stochastic gradient in learning -- Optimal decision sequences -- Learning from image sequences. | |
| 520 | _aThis book provides tools and algorithms for solving a wide class of optimization tasks by learning from their repetitions. A unified framework is provided for learning algorithms that are based on the stochastic gradient (a golden standard in learning), including random simultaneous perturbations and the response surface the methodology. Original algorithms include model-free learning of short decision sequences as well as long sequences-relying on model-supported gradient estimation. Learning is based on whole sequences of a process observation that are either vectors or images. This methodology is applicable to repetitive processes, covering a wide range from (additive) manufacturing to decision making for COVID-19 waves mitigation. A distinctive feature of the algorithms is learning between repetitions-this idea extends the paradigms of iterative learning and run-to-run control. The main ideas can be extended to other decision learning tasks, not included in this book. The text is written in a comprehensible way with the emphasis on a user-friendly presentation of the algorithms, their explanations, and recommendations on how to select them. The book is expected to be of interest to researchers, Ph.D., and graduate students in computer science and engineering, operations research, decision making, and those working on the iterative learning control. | ||
| 988 | _aSpringer_Robotics_2022 | ||
| 650 | 7 |
_2embne _9145705 _aOptimización matemática |
|
| 776 | 0 | 8 |
_iPrinted edition: _z9783030883959 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030883973 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030883980 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-88396-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 998 |
_b03/2023 _dz _eu _zSI |
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