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Fuzzy Logic : Applications in Artificial Intelligence, Big Data, and Machine Learning /
This hands-on guide offers clear explanations of fuzzy logic along with practical uses and detailed examples. Written by an award-winning engineer and experienced author, Fuzzy Logic: Applications in Artificial Intelligence, Big Data, and Machine Learning is aimed at improving competence and skills...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, N.Y. :
McGraw Hill LLC,
[2024]
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| Edición: | First edition. |
| Colección: | McGraw-Hill's AccessEngineeringLibrary.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 020 | |a 9781264676132 (e-ISBN) | ||
| 020 | |a 1264676131 (e-ISBN) | ||
| 020 | |z 9781264675913 (print-ISBN) | ||
| 020 | |z 1264675917 (print-ISBN) | ||
| 035 | |a (OCoLC)1406806141 | ||
| 040 | |a IN-ChSCO |b eng |e rda | ||
| 041 | 0 | |a eng | |
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| 072 | 7 | |a TEC |x 041000 |2 bisacsh | |
| 082 | 0 | 4 | |a 511.313 |2 23 |
| 100 | 1 | |a Tsoukalas, Lefteri H., |e author. | |
| 245 | 1 | 0 | |a Fuzzy Logic : |b Applications in Artificial Intelligence, Big Data, and Machine Learning / |c Lefteri H. Tsoukalas. |
| 250 | |a First edition. | ||
| 264 | 1 | |a New York, N.Y. : |b McGraw Hill LLC, |c [2024] | |
| 264 | 4 | |c ?2024 | |
| 300 | |a 1 online resource (304 pages) : |b 50 illustrations. | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a computer |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 490 | 1 | |a McGraw-Hill's AccessEngineeringLibrary | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- 1 Language and Computation -- References -- 2 The Mathematics of Fuzzy Logic -- 2.1 Notating Fuzzy Sets -- 2.2 Fuzzy Set Operations -- 2.3 Operations with Fuzzy Sets -- 2.4 Alpha Cuts and Resolution -- 2.5 The Extension Principle -- References -- 3 Many-to-Many Mappings in AI and ML -- 3.1 Fuzzy Propositions and if/then Rules -- 3.2 Fuzzy Relations -- 3.3 Operators for Implications and Connectives -- 3.4 Composition and the Compositional Rule of Inference -- References -- 4 Learning and Control -- 4.1 Architecture of Fuzzy Rule Clusters -- 4.2 Constructing a Rule-Based Controller and Interfacing It with Its Environment -- 4.3 Defuzzification Methods -- References -- 5 Forecasting with Fuzzy Algorithms -- 5.1 Forecasting -- 5.2 Fuzzy Logic Forecasting -- 5.3 Predicting Demand with a Probability Model -- References -- 6 Advanced Topics -- 6.1 Fuzzy Logic and AI -- 6.2 Fuzzy Control -- 6.3 Neuro-Fuzzy Control -- 6.4 Digital Twins and Transfer Learning -- 6.5 Cybersecurity -- 6.6 Transfer Learning for Trustworthy and Explainable AI -- 6.7 Medical AI with Deontic and Fuzzy Logic -- References -- A Python Script for Example 4.1 -- B Fuzzy Algorithm for Predicting Power Demand in the Example of Chapter 5 (in Python) -- C Review Questions -- Homework Problems -- Index. | |
| 520 | 0 | |a This hands-on guide offers clear explanations of fuzzy logic along with practical uses and detailed examples. Written by an award-winning engineer and experienced author, Fuzzy Logic: Applications in Artificial Intelligence, Big Data, and Machine Learning is aimed at improving competence and skills in students and professionals alike. Inside, you will discover how to apply fuzzy logic and migrate to a new man-machine relationship in the context of pervasive digitization and big data across emerging technologies. The book lays out real-world applications in intelligent energy systems with demand response, smart homes, electrification of transportation, supply chain efficiencies, smart cities, e-commerce, education, healthcare, and decarbonization. | |
| 530 | |a Also available in print and PDF edition. | ||
| 533 | |a Electronic reproduction. |b New York, N.Y. : |c McGraw Hill, |d 2024. |n Mode of access: World Wide Web. |n System requirements: Web browser. |n Access may be restricted to users at subscribing institutions. | ||
| 538 | |a Mode of access: Internet via World Wide Web. | ||
| 546 | |a In English. | ||
| 588 | |a Description based on e-Publication PDF. | ||
| 650 | 0 | |a Fuzzy logic. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Big data. | |
| 655 | 0 | |a Electronic books. | |
| 776 | 0 | 8 | |i Print version: |t Fuzzy Logic : Applications in Artificial Intelligence, Big Data, and Machine Learning. |b First edition. |d New York, N.Y. : McGraw-Hill Education, 2024 |z 9781264675913 |w (OCoLC)1359038838 |
| 830 | 0 | |a McGraw-Hill's AccessEngineeringLibrary. | |
| 856 | 4 | 0 | |u https://accessengineeringlibrary.uam.elogim.com/content/book/9781264675913 |z Texto completo |