Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning

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 proofsProvides extended and new comprehensive methods which can be used efficiently in a fuzzy environment as well as optimization problems and related fieldsCovers applications and elaborates on the usage of the developed methodology in various fields of industry such as software technologies, biomedicine, image processing, and communications INDICE: Section 1: Decision Making: New Developments1. Neural networks2. Artificial intelligent algorithms, motivation and terminology3. Decision processes4. Learning theorySection 2: Metaheuristic Algorithms5. Nature-inspired algorithms6. Physic-based algorithms7. evolution-based algorithms8. swarm-based algorithms9. Multi-objective algorithms10. Unconstrained / constrained nonlinear optimization11. Evolutionary ComputingSection 3: Optimization Problems12. Mathematical Programming13. Discrete and Combinatorial Optimization14. Optimization and Data Analysis15. Applied optimization problems16. Engineering problemsSection 4: Machine Learning17. Deep Learning18. (Artificial) Neural Networks19. Reinforcement Learning Algorithms20. Classification and clusteringSection 5: Soft Computation21. Uncertainty theory22. Fuzzy sets23. Computation with words24. Soft modelling25. Uncertain optimization models26. Chaos theory and chaotic systemsSection 6: Data Analysis27. Data mining and knowledge discovery28. Categories of techniques of data analysis29. Numerical analysis30. Risk analysisSection 7: Fuzzy Decision System31. Fuzzy Control32. Approximate Reasoning33. Effectiveness in Fuzzy Logics34. Neuro-fuzzy Systems35. Fuzzy rule-based systems

• ISBN: 978-0-443-16147-6
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/08/2024
• Nº Volúmenes: 1
• Idioma: Inglés