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| 001 | 978-3-031-52459-2 | ||
| 003 | DE-He213 | ||
| 005 | 20251030111736.0 | ||
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_a9783031524592 _9978-3-031-52459-2 |
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_a10.1007/978-3-031-52459-2 _2doi |
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_aMAT042000 _2bisacsh |
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| 100 | 1 |
_aToscano, Rosario. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _973665 |
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| 245 | 1 | 0 |
_aSolving Optimization Problems with the Heuristic Kalman Algorithm _h[electronic resource] : _bNew Stochastic Methods / _cby Rosario Toscano. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2024. |
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| 300 |
_aXX, 286 p. 1 illus. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSpringer Optimization and Its Applications, _x1931-6836 ; _v212 |
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| 505 | 0 | _a1 Introduction -- 2 Stochastic Optimization Methods -- 3 Heuristic Kalman Algorithm -- 4 Some Notions on System Modeling -- 5 Robust Control of Uncertain Parametric Systems -- 6 Preventive Maintenance -- 7 Machine Learning -- 8 Conclusion -- A Signal and System Norms -- B Convergence Properties of the HKA and Program Code -- References -- Index. | |
| 520 | _aThis text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering design, namely non-convex optimization problems. The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems. The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more. The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design. . | ||
| 650 | 0 |
_aMathematical optimization. _965522 |
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| 650 | 0 |
_aAlgorithms. _958677 |
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| 650 | 1 | 4 |
_aOptimization. _965528 |
| 650 | 2 | 4 |
_aAlgorithms. _958677 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
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_iPrinted edition: _z9783031524585 |
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_iPrinted edition: _z9783031524608 |
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_iPrinted edition: _z9783031524615 |
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_aSpringer Optimization and Its Applications, _x1931-6836 ; _v212 _973211 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-52459-2 |
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