Decision-Making Models
A Perspective of Fuzzy Logic and Machine Learning
- 1st Edition - July 24, 2024
- Latest edition
- Editors: Tofigh Allahviranloo, Witold Pedrycz, Amir Seyyedabbasi
- Language: English
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 a… Read more
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Description
Description
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.
Key features
Key features
- Introduces mathematics of intelligent systems which provides the usage of mathematical rigor such as precise definitions, theorems, results, and proofs
- Provides extended and new comprehensive methods which can be used efficiently in a fuzzy environment as well as optimization problems and related fields
- Covers applications and elaborates on the usage of the developed methodology in various fields of industry such as software technologies, biomedicine, image processing, and communications
Readership
Readership
Table of contents
Table of contents
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: July 25, 2024
- Language: English
About the editors
About the editors
TA
Tofigh Allahviranloo
WP
Witold Pedrycz
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.
AS