Intelligent Systems II · TEE · mocks
Q7(a) Applications of fuzzy logic [10]
Re-examination · 31 Jan 2026
100-mark timed practice using the actual Re-exam 2026 paper. Q1 compulsory; any four of Q2–Q7. Worked solutions are below the questions.
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
- 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.
- 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.
- Fuzzification. Sensor readings are converted to membership degrees, e.g. dirt 60% → Medium 0.8, High 0.2.
- 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.
- Inference. Mamdani min–max: rule strength = min of the two memberships; outputs combined by max.
- 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.
- 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.