Hardcover. 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. This bookpresents a broad variety of different contemporary IT methods and applications inIntelligent Systems is displayed. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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D�tails bibliographiques

Titre: Innovative Issues in Intelligent Systems (Hardcover)
�diteur: Springer International Publishing AG, Cham
Date de parution: 2016
Langue: anglais
ISBN � 10 chiffres: 3319272667
ISBN � 13 chiffres: 9783319272665
Reliure: Hardcover
�dition: Edition originale
�tat de l'article: new

A propos de ce titre

Synopsis

A realistic Overview of Intelligent Systems in Industry.- Prediction of the Attention Area in Ambient Intelligence Tasks Using Adaptive Neuro-Fuzzy Inference System (ANFIS).- Chapter III. Integration of Knowledge Components in Hybrid Intelligent Control Systems.- Learning Intelligent Controls in High Speed Networks: Synergies of Computational Intelligence with Control and Q-Learning Theories.- Logical Operations and Inference in the Complex S-logic.- Generalized Nets as Tools for Modelling of Data Mining Processes.- Induction of Modular Classification Rules by Information Entropy Based Rule Generation.- Proposals for Knowledge Driven and Data Driven Applications in Security Systems.- On intuitionistic Fuzzy Operators from Modal Types.- Uncertain Switched Fuzzy Systems: A Robust Output Feedback Control Design.- Multistep Modeling for Approximation and Classification by Use of RBF Network Models.

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