Innovative Issues in Intelligent Systems

Herausgegeben:Sgurev, Vassil; Yager, Ronald R; Kacprzyk, Janusz; Jotsov, Vladimir

• Broschiertes Buch

Produktbeschreibung

This bookpresents a broad variety of different contemporary IT methods and applications inIntelligent Systems is displayed. Every book chapter represents a detailed,specific, far reaching and original re-search in a respective scientific andpractical field. However, all of the chapters share the common point of strongsimilarity in a sense of being innovative, applicable and mutually compatiblewith each other. In other words, the methods from the different chapters can beviewed as bricks for building the next generation "thinking machines" as wellas for other futuristic logical applications that are rapidly changing ourworld nowadays.

Produktdetails

• Studies in Computational Intelligence 623
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-80104-9
• Softcover reprint of the original 1st ed. 2016
• Seitenzahl: 364
• Erscheinungstermin: 30. März 2018
• Englisch
• Abmessung: 235mm x 155mm x 20mm
• Gewicht: 557g
• ISBN-13: 9783319801049
• ISBN-10: 331980104X
• Artikelnr.: 53570667

Inhaltsangabe

A realistic Overview of Intelligent Systems in Industry.-Prediction of the Attention Area in Ambient Intelligence Tasks Using AdaptiveNeuro-Fuzzy Inference System (ANFIS).- Chapter III. Integration of KnowledgeComponents in Hybrid Intelligent Control Systems.- Learning IntelligentControls in High Speed Networks: Synergies of Computational Intelligence withControl and Q-Learning Theories.- Logical Operations and Inference in theComplex S-logic.- Generalized Nets as Tools for Modelling of Data MiningProcesses.- Induction of Modular Classification Rules by Information EntropyBased Rule Generation.- Proposals for Knowledge Driven and Data DrivenApplications in Security Systems.- On intuitionistic Fuzzy Operators from ModalTypes.- Uncertain Switched Fuzzy Systems: A Robust Output Feedback ControlDesign.- Multistep Modeling for Approximation and Classification by Use of RBFNetwork Models.