Self-Evolvable Systems

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This book presents key methods to successfully manage the growing complexity of systems where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are required.

This monograph presents key method to successfully manage the growing complexity of systems where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are nowadays required. The transition from adaptable to evolvable and finally to self-evolvable systems is highlighted, self-properties such as self-organization, self-configuration, and self-repairing are introduced and challenges and limitations of the self-evolvable engineering systems are evaluated.


Introduces the paradigm of self-evolvability enabling the reader to combine technological, socioeconomical, and scientific aspects of complexity into one concentrated expression Emphasizing the multiple features involved by self-evolvability, with a focus on innovative concepts and models Includes various case studies representing a source of inspiration for emerging methods, technologies and systems in their needed transition from adaptable and evolvable towards self-evolvable

Inhalt
Introduction.- General Framework.- Differential Models.- Informational Criteria.- Self-Evolvability for Physical and Chemical Systems.- Self-Evolvability for Biosystems.- Self-Evolvability for Cognitive Systems.- Control Systems.- Manufacturing Systems.- Concept Lattices.- Design of Experiments.- Perspectives.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783642431494
    • Auflage 2012
    • Sprache Englisch
    • Genre Allgemeines & Lexika
    • Lesemotiv Verstehen
    • Größe H235mm x B155mm x T17mm
    • Jahr 2014
    • EAN 9783642431494
    • Format Kartonierter Einband
    • ISBN 3642431496
    • Veröffentlichung 09.08.2014
    • Titel Self-Evolvable Systems
    • Autor Octavian Iordache
    • Untertitel Machine Learning in Social Media
    • Gewicht 458g
    • Herausgeber Springer Berlin Heidelberg
    • Anzahl Seiten 300

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