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About Evolving Intelligent Systems: Methodology and Applications (IEEE Press Series on Computational Intelligence)
From theory to techniques, the first all-in-one resource for EISThere 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 emphasis on the balance between novel theoretical results and solutions and practical real-life applications. Explains the following fundamental approaches for developing evolving intelligent systems (EIS):the Hierarchical Prioritized Structurethe Participatory Learning Paradigmthe Evolving Takagi-Sugeno fuzzy systems (eTS+)the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithmEmphasizes the importance and increased interest in online processing of data streams Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulationPresents a methodology for developing robust and interpretable evolving fuzzy rule-based systemsIntroduces an integrated approach to incremental (real-time) feature extraction and classificationProposes a study on the stability of evolving neuro-fuzzy recurrent networksDetails methodologies for evolving clustering and classificationReveals different applications of EIS to address real problems in areas of:evolving inferential sensors in chemical and petrochemical industrylearning and recognition in roboticsFeatures downloadable software resources Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.
Detailed Information
| Author: | Plamen Angelov, Dimitar P. Filev, Nik Kasabov |
|---|---|
| Publication Year: | 1 |
| ISBN: | 9780470287194 |
| Pages: | 462 |
| Language: | English |
| File Size: | 6.538 |
| Format: | |
| Price: | FREE |
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