Fuzzy_Logic_PPT_Final.pptx for college ege

it complex logic

1. Fuzzy Logic: Concepts and Applications Prepared by: Pravanjan Sahoo

2. What is Fuzzy Logic? • A form of multi-valued logic that deals with approximate reasoning. • Unlike classical binary logic (0 or 1), fuzzy logic allows values between 0 and 1. • Mimics human decision-making by handling uncertainty, vagueness, and imprecision. Example: Classical logic → 'Room is hot (1) or not hot (0)'. Fuzzy logic → 'Room is 0.7 hot and 0.3 cool'.

3. Brief History • Introduced in 1965 by Lotfi A. Zadeh (UC Berkeley). • Proposed to model imprecise concepts humans use in natural language. • Initially criticized but later adopted widely in control systems, AI, and engineering. • Lotfi Zadeh is known as the 'Father of Fuzzy Logic'.

4. Classical Logic vs Fuzzy Logic Classical Logic (Boolean): • Works with only two values → 0 (False) or 1 (True). • Rigid decision-making. • Example: 'Is the glass full?' → Yes (1) or No (0). Fuzzy Logic: • Works with values between 0 and 1. • Allows partial truth (e.g., 0.2, 0.5, 0.9). • Example: 'Glass is 0.6 full'. Real-world Uncertainty Handling: • Handles vague concepts (tall, cold, fast). • Bridges gap between math models and human reasoning. • Used in washing machines, ACs, AI decision-making.

5. Key Concepts • Membership Functions • Linguistic Variables • Fuzzy Sets • Truth Values (0–1 continuum)

6. Fuzzy Inference System (FIS) • Fuzzification • Rule Base (If–Then rules) • Inference Engine • Defuzzification

7. Example: Room Temperature Control Classical Logic: • Room is hot (1) or not hot (0). Fuzzy Logic: • Room temperature can be 0.7 hot and 0.3 cool. • AC adjusts gradually instead of on/off.

8. Applications of Fuzzy Logic • Control Systems (washing machines, ACs, elevators) • AI & Robotics • Decision-making & Expert Systems • Pattern Recognition

9. Advantages • Handles imprecision • Human-like reasoning • Flexible

10. Limitations • Computationally heavy • Requires domain expertise for rule design • Not always optimal

11. Conclusion • Fuzzy Logic provides a way to handle uncertainty and vagueness. • Bridges gap between mathematical precision and human reasoning. • Future scope in AI, ML, robotics, and intelligent systems.

12. References • Lotfi A. Zadeh, 'Fuzzy Sets', 1965. • IEEE Transactions on Fuzzy Systems. • Research papers and online resources.