Learning Systems: From Theory to Practice

By presenting the latest advances in fuzzy sets and computing with words from around the globe, this book disseminates recent innovations in advanced intelligent technologies and systems. From intelligent control and intuitionistic fuzzy quantifiers to various data science and industrial applications, it includes a wide range of valuable lessons learned and ideas for future intelligent products and systems.

Table of Contents

Chapter 1. From von Neumann Architecture and Atanasoffs ABC to Neuro-Morphic Computation and Kasabov’s NeuCube: Principles and Implementations
Chapter 2. Applications of Computational Intelligence in Industrial and Environmental Scenarios
Chapter 3. Real, Imaginary and Complex Sets
Chapter 4. Intercriteria Analysis over Patterns
Chapter 5. From Primary to Conventional Science
Chapter 6. Intelligent Two-Level Optimization and Model Predictive Control of Degrading Plants
Chapter 7. Collaborative Clustering: New Perspective to Rank Factor Granules
Chapter 8. Learning Through Constraint Applications
Chapter 9. Autonomous Flight Control and Precise Gestural Positioning of a Small Quadrotor
Chapter 10. Introduction to the Theory of Randomized Machine Learning
Chapter 11. Grid-Type Fuzzy Models for Performance Evaluation and Condition Monitoring of Photovoltaic Systems
Chapter 12. Intelligent Control of Uncertain Switched Nonlinear Plants: NFG Optimum Control Synthesis via Switched Fuzzy Time-Delay Systems
Chapter 13. Multidimensional Intuitionistic Fuzzy Quantifiers and Level Operators
Chapter 14. Data Processing and Harmonization for Intelligent Transportation Systems: An Application Scenario on Highway Traffic Flows