000			03234nam a2200277za04500
001			17554
008			050703s2011 gw eng d
020			_a9783642180873 99783642180873
082			_a519 _b223
100			_aLughofer, Edwin. _eauthor. _934666
245			_aEvolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications _h[electronic resource] / _cby Edwin Lughofer.
300			_aXXIV, 456 p. _bonline resource.
490			_aStudies in Fuzziness and Soft Computing
490			_x-1434-9922 ; _v-266
505			_aI. Introduction -- Part I - Basic Methodologies -- II. Basic Algorithms for EFS -- III. EFS Approaches for Regression and Classification -- Part II - Advanced Concepts -- IV. Towards Robust and Process-Save EFS -- V. On Improving Performance and Increasing Useability of EFS -- VI. Interpretability Issues in EFS -- Part III. Applications -- VII. Online System Identification and Prediction -- VIII. On-Line Fault and Anomaly Detection -- IX. Visual Inspection Systems -- X. Further (Potential) Application Fields -- Epilog - Achievements, Open Problems and New Challenges in EFS.
520			_aIn today's real-world applications, there is an increasing demand of integrating new information and knowledge on-demand into model building processes to account for changing system dynamics, new operating conditions, varying human behaviors or environmental influences. Evolving fuzzy systems (EFS) are a powerful tool to cope with this requirement, as they are able to automatically adapt parameters, expand their structure and extend their memory on-the-fly, allowing on-line/real-time modeling. This book comprises several evolving fuzzy systems approaches which have emerged during the last decade and highlights the most important incremental learning methods used. The second part is dedicated to advanced concepts for increasing performance, robustness, process-safety and reliability, for enhancing user-friendliness and enlarging the field of applicability of EFS and for improving the interpretability and understandability of the evolved models. The third part underlines the usefulness and necessity of evolving fuzzy systems in several online real-world application scenarios, provides an outline of potential future applications and raises open problems and new challenges for the next generation evolving systems, including human-inspired evolving machines. The book includes basic principles, concepts, algorithms and theoretic results underlined by illustrations. It is dedicated to researchers from the field of fuzzy systems, machine learning, data mining and system identification as well as engineers and technicians who apply data-driven modeling techniques in real-world systems.
650			_aEngineering. _996
650			_aArtificial intelligence. _933648
650			_aEngineering. _996
650			_aArtificial Intelligence (incl. Robotics). _923200
650			_933564 _aAPPL.MATHEMATICS./ COMPUTATIONAL METHODS OF ENGINNEERING.
650			_934018 _aENGINEERING MATHEMATICS
710			_aSpringerLink (Online service) _9111
856			_uhttp://springer.escuelaing.metaproxy.org/book/10.1007/978-3-642-18087-3 _yir a documento _qURL
942			_2ddc _cCF
999			_c14179 _d14179