Anbieter: Studibuch, Stuttgart, Deutschland
hardcover. Zustand: Gut. 452 Seiten; 9781441980199.3 Gewicht in Gramm: 1.
Zustand: New.
Hardcover. Zustand: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.
Sprache: Englisch
Verlag: VDM Verlag Dr. Müller E.K. Nov 2013, 2013
ISBN 10: 383648465X ISBN 13: 9783836484657
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. Neuware -Nowadays data-driven models become more and more an essential part in industrial systems for application tasks such as system identification and analysis, prediction, control or fault detection. Data driven models are mathematical models which are completely identified from data, which can be available in form of offline data sets, most commonly stored in data matrices, or in form of online measurements. Data-driven models possess the nice property that they can be built up generically in the sense that no underlying physical, chemical etc. laws about the system variables have to be known. Whenever measurements are recorded online with a certain frequency, usually the models should be kept up-to-date, especially when new system states occur during online production processes. This requires an adaptation of model parameters and an evolution of model structures with incremental learning steps, as a complete rebuilding from time to time with all recorded measurements would not terminate in real-time. The book addresses the on-line evolution of fuzzy models and underlines its necessity by concrete application examples from on-line quality control systems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch.
Sprache: Englisch
Verlag: VDM Verlag Dr. Müller e.K., 2013
ISBN 10: 383648465X ISBN 13: 9783836484657
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Evolving Fuzzy Models | Incremental Learning, Interpretability and Stability Issues, Applications | Edwin Lughofer | Taschenbuch | 156 S. | Englisch | 2013 | VDM Verlag Dr. Müller e.K. | EAN 9783836484657 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 117,81
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 156 pages. 8.66x5.91x0.36 inches. In Stock.
Sprache: Englisch
Verlag: Berlin ; Heidelberg : Springer, 2011
ISBN 10: 3642180868 ISBN 13: 9783642180866
Anbieter: Druckwaren Antiquariat, Salzwedel, Deutschland
Verbandsmitglied: GIAQ
OPp., gebundene Ausgabe. Zustand: Gut. XXIV, 454 S.: graph. Darst. ; 24 cm, Cover with little wear, good condition. ISBN: 9783642180866 Altersfreigabe FSK ab 0 Jahre Sprache: Englisch Gewicht in Gramm: 1050.
Anbieter: Buchpark, Trebbin, Deutschland
Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 452 | Sprache: Englisch | Produktart: Bücher | Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research. .
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 158,19
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 158,19
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 158,19
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 174,21
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 158,18
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New.
Zustand: New.
Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 480 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 480 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Anbieter: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irland
Zustand: New.
Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 224,00
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Sprache: Englisch
Verlag: Springer New York, Springer US Apr 2012, 2012
ISBN 10: 1441980199 ISBN 13: 9781441980199
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Buch. Zustand: Neu. Neuware -Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy.Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations.This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 452 pp. Englisch.
Sprache: Englisch
Verlag: Springer Berlin Heidelberg, 2013
ISBN 10: 3642266924 ISBN 13: 9783642266928
Anbieter: moluna, Greven, Deutschland
EUR 180,07
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Sprache: Englisch
Verlag: Springer New York, Springer New York, 2014
ISBN 10: 1489993401 ISBN 13: 9781489993403
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
Sprache: Englisch
Verlag: Springer New York, Springer US, 2012
ISBN 10: 1441980199 ISBN 13: 9781441980199
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
Sprache: Englisch
Verlag: Springer Berlin Heidelberg, 2011
ISBN 10: 3642180868 ISBN 13: 9783642180866
Anbieter: moluna, Greven, Deutschland
EUR 180,07
Anzahl: Mehr als 20 verfügbar
In den WarenkorbGebunden. Zustand: New.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 217,09
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 216,49
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 217,44
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Springer International Publishing, 2019
ISBN 10: 3030056449 ISBN 13: 9783030056445
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet ofThings. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.
Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 237,51
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: As New. Unread book in perfect condition.