Innovative Issues in Intelligent Systems

  • Sha J
  • Angelov P
  • Umair M
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Abstract

This book presents a broad variety of different contemporary IT methods and applications in Intelligent Systems is displayed. Every book chapter represents a detailed, specific, far reaching and original re-search in a respective scientific and practical field. However, all of the chapters share the common point of strong similarity in a sense of being innovative, applicable and mutually compatible with each other. In other words, the methods from the different chapters can be viewed as bricks for building the next generation “thinking machines” as well as for other futuristic logical applications that are rapidly changing our world nowadays. 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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APA

Sha, J., Angelov, P., & Umair, M. (2016). Innovative Issues in Intelligent Systems, 623. Retrieved from http://link.springer.com/10.1007/978-3-319-27267-2

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