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Titolo: | Evolving intelligent systems : methodology and applications / / Plamen Angelov, Dimitar P. Filev, Nik Kasabov |
Pubblicazione: | Hoboken, New Jersey : , : John Wiley, , c2010 |
[Piscataqay, New Jersey] : , : IEEE Xplore, , [2010] | |
Descrizione fisica: | 1 online resource (462 p.) |
Disciplina: | 006.3 |
Soggetto topico: | Computational intelligence |
Fuzzy systems | |
Neural networks (Computer science) | |
Evolutionary programming (Computer science) | |
Intelligent control systems | |
Altri autori: | KasabovNikola K FilevDimitar P. <1959-> AngelovPlamen |
Note generali: | Includes index. |
Nota di bibliografia: | Includes bibliographical references and index. |
Nota di contenuto: | PREFACE -- Evolving Intelligent Systems -- The Editors -- PART I: METHODOLOGY -- Evolving Fuzzy Systems -- 1. Learning Methods for Evolving Intelligent Systems (R. Yager) -- 2. Evolving Takagi-Sugeno Fuzzy Systems from Data Streams (eTS+) (P. Angelov) -- 3. Fuzzy Models of Evolvable Granularity (W. Pedrycz) -- 4. Evolving Fuzzy Modeling Using Participatory Learning (E. Lima, M. Hell, R. Ballini, and F. Gomide) -- 5. Towards Robust and Transparent Evolving Fuzzy Systems (E. Lughofer) -- 6. The building of fuzzy systems in real-time: towards interpretable fuzzy rules (A. Dourado, C. Pereira, and V. Ramos) -- Evolving Neuro-Fuzzy Systems -- 7. On-line Feature Selection for Evolving Intelligent Systems (S. Ozawa, S. Pang, and N. Kasabov) -- 8. Stability Analysis of an On-Line Evolving Neuro-Fuzzy Network (J. de J. Rubio Avila) -- 9. On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems (G. Prasad, T. M. McGinnity, and G. Leng) -- 10. Data Fusion via Fission for the Analysis of Brain Death (L. Li, Y. Saito, D. Looney, T. Tanaka, J. Cao, and D. Mandic) -- Evolving Fuzzy Clustering and Classification -- 11. Similarity Analysis and Knowledge Acquisition by Use of Evolving Neural Models and Fuzzy Decision (G. Vachkov) -- 12. An Extended version of Gustafson-Kessel Clustering Algorithm for Evolving Data Stream Clustering (D. Filev, and O. Georgieva) -- 13. Evolving Fuzzy Classification of Non-Stationary Time Series (Y. Bodyanskiy, Y. Gorshkov, I. Kokshenev, and V. Kolodyazhniy) -- PART II: APPLICATIONS OF EIS -- 14. Evolving Intelligent Sensors in Chemical Industry (A. Kordon et al.) -- 15. Recognition of Human Grasps by Fuzzy Modeling (R Palm, B Kadmiry, and B Iliev) -- 16. Evolutionary Architecture for Lifelong Learning and Real-time Operation in Autonomous Robots (R. J. Duro, F. Bellas and J.A. Becerra) 17. Applications of Evolving Intelligent Systems to Oil and Gas Industry (J. J. Macias Hernandez et al.) -- Conclusion. |
Sommario/riassunto: | From theory to techniques, the first all-in-one resource for EIS There is a clear demand in advanced process industries, defense, and Internet and communication (VoIP) applications for intelligent yet adaptive/evolving systems. Evolving Intelligent Systems is the first self- contained volume that covers this newly established concept in its entirety, from a systematic methodology to case studies to industrial applications. Featuring chapters written by leading world experts, it addresses the progress, trends, and major achievements in this emerging research field, with a strong emphas |
Titolo autorizzato: | Evolving intelligent systems |
ISBN: | 1-282-54874-3 |
9786612548741 | |
0-470-56996-4 | |
0-470-56995-6 | |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910140608203321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |