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Fuzzy mathematics in artificial intelligence [#992296]
Free Download Fuzzy mathematics in artificial intelligence: Concepts applications and soft computing approaches (Math and Artificial Intelligence) by Anshuman Mishra
English | August 22, 2025 | ISBN: N/A | ASIN: B0FNKHSMJC | 555 pages | EPUB | 0.37 Mb
Artificial Intelligence (AI) has become one of the most transformative fields of modern science and technology. From self-driving cars and voice assistants to healthcare diagnosis and financial forecasting, AI has entered almost every domain of human life. While classical AI methods rely heavily on binary logic and deterministic models, real-world problems often involve uncertainty, imprecision, and vagueness. For example, when we say "the weather is hot" or "the patient has a mild fever," these are not crisp, binary statements. They are subjective, imprecise, and fuzzy in nature. Traditional mathematical tools struggle to capture this vagueness effectively.
This is where Fuzzy Mathematics plays a revolutionary role. Fuzzy sets, fuzzy logic, and fuzzy controllers provide a framework to model and compute with such imprecise and linguistic information. Instead of forcing decisions into rigid categories like "true or false" or "yes or no," fuzzy logic allows reasoning in degrees, closer to the way humans think and make judgments.
The integration of Fuzzy Mathematics into AI leads to Soft Computing, a powerful paradigm that combines fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning. This book is written with the vision of providing students with a comprehensive introduction to Fuzzy Mathematics and its applications in Artificial Intelligence and Soft Computing systems.
2. Importance of Fuzzy Mathematics in Artificial Intelligence
Fuzzy Mathematics is not just an optional tool for AI-it is a necessity for solving many real-life problems where binary logic fails. Consider the following scenarios:Medical Diagnosis: A doctor may say a patient's blood pressure is "slightly high." Such linguistic descriptions cannot be captured in strict numbers but can be modeled using fuzzy sets.Robotics: A robot deciding whether a surface is "slippery" or "safe" cannot always rely on exact numerical thresholds. Fuzzy logic allows smoother decision-making.Weather Forecasting: Meteorological predictions often deal with uncertain and imprecise data. Fuzzy systems provide a better way to model such variability.Finance and Business: Risk analysis, customer satisfaction, and stock market predictions involve vagueness, which fuzzy mathematics can handle effectively.In all these areas, AI systems need to mimic human-like reasoning under uncertainty. Fuzzy Mathematics bridges the gap between human reasoning and machine computation.
The importance of this book lies in the fact that it brings together the theoretical foundations of fuzzy mathematics and the practical applications of fuzzy logic in AI systems. Students not only learn the mathematics behind fuzziness but also understand how to apply it in AI algorithms, controllers, and decision-making models.
3. Key Concepts Covered in the Book (General Overview)
This book covers the following fundamental aspects of fuzzy mathematics and AI in a progressive manner, starting from basic principles and moving towards advanced applications:Fuzzy Sets and Membership Functions: Understanding how fuzzy sets differ from crisp sets, and how membership functions represent degrees of belonging.Fuzzy Logic: Exploring the foundations of fuzzy reasoning, linguistic variables, and truth values beyond 0 and 1.Fuzzy Relations and Fuzzy Arithmetic: Learning how to perform operations on fuzzy numbers and how fuzzy relations can model complex systems.
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