Decision-Making Models

A Perspective of Fuzzy Logic and Machine Learning
Erscheinungsjahr 2024
ISBN: 978-0-443-16147-6
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems.

Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.

Autoren/Hrsg.

Allahviranloo, Tofigh

Pedrycz, Witold

Seyyedabbasi, Amir

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung

Inhaltsverzeichnis

Section 1: Decision Making: New Developments
1. Neural networks
2. Artificial intelligent algorithms, motivation and terminology
3. Decision processes
4. Learning theory

Section 2: Metaheuristic Algorithms
5. Nature-inspired algorithms
6. Physic-based algorithms
7. evolution-based algorithms
8. swarm-based algorithms
9. Multi-objective algorithms
10. Unconstrained / constrained nonlinear optimization
11. Evolutionary Computing

Section 3: Optimization Problems
12. Mathematical Programming
13. Discrete and Combinatorial Optimization
14. Optimization and Data Analysis
15. Applied optimization problems
16. Engineering problems

Section 4: Machine Learning
17. Deep Learning
18. (Artificial) Neural Networks
19. Reinforcement Learning Algorithms
20. Classification and clustering

Section 5: Soft Computation
21. Uncertainty theory
22. Fuzzy sets
23. Computation with words
24. Soft modelling
25. Uncertain optimization models
26. Chaos theory and chaotic systems

Section 6: Data Analysis
27. Data mining and knowledge discovery
28. Categories of techniques of data analysis
29. Numerical analysis
30. Risk analysis

Section 7: Fuzzy Decision System
31. Fuzzy Control
32. Approximate Reasoning
33. Effectiveness in Fuzzy Logics
34. Neuro-fuzzy Systems
35. Fuzzy rule-based systems

Über Autor(innen)

Seyyedabbasi, Amir
Amir Seyyedabbasi is an assistant professor of software engineering at Istinye University, Turkey. He received his B.Sc., M.Sc., and Ph.D. degrees in computer engineering. His research interests include optimization algorithms, routing protocol design in wireless sensor networks, and IoT. Dr. Seyyedabbasi has several articles in Springer and Elsevier. He serves as a reviewer in some respected journals. He aims to develop and propose new and the hybrid optimization algorithm in engineering.

Allahviranloo, Tofigh
Tofigh Allahviranloo is a full professor of applied mathematics at Istinye University, Turkey. As a trained mathematician and computer scientist, Prof. Allahviranloo has developed a passion for multi- and interdisciplinary research. He is not only deeply involved in fundamental research in fuzzy applied mathematics, especially fuzzy differential equations, but he also aims at innovative applications in the applied biological sciences. He is the author of several books and many papers published by Elsevier and Springer. He actively serves the research community, as Editor-in-Chief of the International J. of Industrial Mathematics, and Associate Editor or editorial board member of several other journals, including Information Sciences, Fuzzy Sets and Systems, Journal of Intelligent and Fuzzy Systems, Iranian J. of Fuzzy Systems and Mathematical Sciences.

Pedrycz, Witold
Dr. Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in computational intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. In 2012 he was elected a fellow of the Royal Society of Canada. His main research directions involve computational intelligence, fuzzy modeling and granular computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and control engineering. He is also an author of 18 research monographs and edited volumes covering various aspects of computational intelligence, data mining, and software engineering. Dr. Pedrycz is vigorously involved in editorial activities. He is the editor-in-chief of Information Sciences, editor-in-chief of WIREs Data Mining and Knowledge Discovery, and co-editor-in-chief of International Journal of Granular Computing, and Journal of Data Information and Management. He serves on the advisory board of IEEE Transactions on Fuzzy Systems.