# Q7(a) Applications of fuzzy logic [10]

Subject: Intelligent Systems II

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/is2/#tee/papers/paper-reexam-2026/q7a

Canonical: https://deep-learning-study-room.vercel.app/read/is2/tee/papers/paper-reexam-2026--q7a/

Actual TEE re-exam · 100 marks · 3 hours · Q1 compulsory + any 4 of Q2–Q7 · every answer verified (key errors flagged)

**List the applications of fuzzy logic. Explain any one application in detail.** [10 marks]

### Answer

### Definition

Fuzzy logic is a multi-valued logic, introduced by Lotfi Zadeh (1965), in which truth values range over [0, 1]; it lets machines reason with vague, human-like terms such as “slightly dirty” or “very hot”.

### Applications

| Area | Examples |
| --- | --- |
| Home appliances | Washing machines (wash time from dirt and load), air conditioners, refrigerators, rice cookers, vacuum cleaners |
| Automotive | Automatic transmission, anti-lock braking (ABS), cruise control, engine control |
| Transportation | Traffic-signal control, Sendai subway train control (smooth braking and acceleration) |
| Consumer electronics | Camera autofocus and auto-exposure, image stabilisation |
| Industrial control | Cement kilns, pH, temperature and distillation control in chemical plants |
| Aerospace | Satellite and spacecraft attitude control |
| Medicine | Diagnosis support, anaesthesia dosage control |
| Finance and decisions | Loan approval, credit and fire-risk scoring, stock trading rules |
| AI and robotics | Robot navigation, natural-language interfaces, expert systems |

### One application in detail: fuzzy washing machine

1. **Problem.** Wash time depends on how dirty and how greasy the clothes are. These are vague (“a bit dirty”), and a fixed timer wastes water and power.
2. **Inputs and outputs.** Sensors measure dirt (turbidity of water) and grease; the output is wash time. Each is described by fuzzy sets: dirt Small/Medium/High, grease Small/Medium/High, time Very Short … Very Long.
3. **Fuzzification.** Sensor readings are converted to membership degrees, e.g. dirt 60% → Medium 0.8, High 0.2.
4. **Rule base.** 9 expert rules, e.g. IF dirt is High AND grease is High THEN time is Very Long; IF dirt is Small AND grease is Small THEN time is Very Short.
5. **Inference.** Mamdani min–max: rule strength = min of the two memberships; outputs combined by max.
6. **Defuzzification.** Use the specified defuzzifier. For a separate representative-weighted illustration, strengths [.6,.4,.2] at times [25,40,60] give (.6×25+.4×40+.2×60)/(.6+.4+.2)=(15+16+12)/1.2≈35.833 minutes. These illustrative strengths are supplied here, not derived from unspecified input graphs. A Mamdani area centroid requires the full aggregate shape.
7. **Benefits.** Smooth adjustment to every load, saves water and electricity, easy to design from expert knowledge without an exact mathematical model.

### Advantages and disadvantages (bonus)

**Advantages:** handles imprecise or noisy data; mimics human reasoning; easy to design and modify; no exact model needed; low computational cost. **Disadvantages:** rules and membership functions depend on expert judgement; results are hard to verify formally; tuning can take time; not suited where very high precision is required.

**Yaad rakho:** Application explain karte waqt 5 cheezein: inputs, fuzzy sets, rules, inference (min/max), defuzzification + ek number ka example.