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1. |
Record Nr. |
UNINA990005374220403321 |
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Autore |
Maringer, Johannes |
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Titolo |
L'homme prèhistorique et ses dieux / Johannes Maringer |
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Pubbl/distr/stampa |
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Descrizione fisica |
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302 p., 59 tav. : ill. ; 21 cm |
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Collana |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNISALENTO991002219559707536 |
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Autore |
Kawohl, Bernhard |
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Titolo |
Optimal shape design [e-book] : lectures given at the joint C.I.M./C.I.M.E. Summer school held in Tróia, Portugal, June 1-6, 1998 / by Bernhard Kawohl ... [et al.] ; edited by Arrigo Cellina, Antonio Ornelas |
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Pubbl/distr/stampa |
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ISBN |
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Descrizione fisica |
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1 online resource (xii, 392 p.) |
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Collana |
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Lecture Notes in Mathematics, 0075-8434 ; 1740 |
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Altri autori (Persone) |
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Cellina, Arrigo |
Ornelas, Antonio |
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Disciplina |
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Soggetti |
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Mathematics |
Global analysis (Mathematics) |
Mathematical optimization |
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Lingua di pubblicazione |
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Formato |
Risorsa elettronica |
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Livello bibliografico |
Monografia |
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3. |
Record Nr. |
UNINA9910788612503321 |
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Autore |
Kamber Franz W. |
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Titolo |
Invariant differential operators and the cohomology of Lie algebra sheaves / / by Franz W. Kamber and Philippe Tondeur |
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Pubbl/distr/stampa |
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Providence : , : American Mathematical Society, , 1971 |
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ISBN |
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Descrizione fisica |
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1 online resource (131 p.) |
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Collana |
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Memoirs of the American Mathematical Society ; ; number 113 |
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Disciplina |
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Soggetti |
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Sheaf theory |
Homology theory |
Differential operators |
Lie algebras |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Bibliography: pages 122-124. |
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Nota di contenuto |
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""Contents""; ""Introduction""; ""Part I""; ""1. Lie algebra sheaves of vectorfields""; ""2. Invariant differential operators""; ""3. The universal envelope of a sheaf of twisted Lie algebras""; ""4. Cohomology of sheaves of twisted Lie algebras""; ""Part II""; ""5. Group actions""; ""6. Transitive Lie algebra sheaves""; ""7. Cohomology of transitive sheaves""; ""8. Invariant connections on locally homogeneous spaces""; ""9. Explicite computations""; ""Appendix""; ""References"" |
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4. |
Record Nr. |
UNINA9910813585903321 |
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Autore |
Kagan Eugene |
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Titolo |
Autonomous mobile robots and multi-robot systems : motion-planning, communication and swarming / / edited by Eugene Kagan, Nir Shvalb, Irad Ben-Gal |
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Pubbl/distr/stampa |
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Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2020] |
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©2020 |
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ISBN |
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1-119-21316-9 |
1-5231-3281-7 |
1-119-21317-7 |
1-119-21315-0 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (343 pages) |
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Disciplina |
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Soggetti |
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Mobile robots - Automatic control |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Motion-planning schemes in global coordinates -- Basic perception -- Motion in the global coordinates -- Motion in potential field and navigation function -- GNSS and robot localization -- Motion in local coordinates -- Motion in unknown environment -- Energy limitations and energetic efficiency of mobile robots -- Multi-robot systems and swarming -- Collective motion with shared environment map -- Collective motion with direct and indirect communication -- Brownian motion and swarm dynamics -- Conclusions. |
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Sommario/riassunto |
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Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly |
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implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot’s location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot's expectations and knowledge. The robot's motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions. Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues. |
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5. |
Record Nr. |
UNINA9910350230503321 |
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Autore |
Zhou Zhi-Hua |
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Titolo |
Evolutionary Learning: Advances in Theories and Algorithms / / by Zhi-Hua Zhou, Yang Yu, Chao Qian |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XII, 361 p. 59 illus., 20 illus. in color.) |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Algorithms |
Computer science—Mathematics |
Artificial Intelligence |
Algorithm Analysis and Problem Complexity |
Math Applications in Computer Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration. . |
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Sommario/riassunto |
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Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well |
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received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance. . |
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