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.

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

Tartalomjegyzék:

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

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