fuzzy-logic

## What I do - Design fuzzy inference systems - Implement membership functions - Create fuzzy rules - Handle uncertainty and imprecision - Build expert systems with fuzzy logic - Control systems with fuzzy rules ## When to use me Use me when: - Building control systems - Working with imprecise inputs - Expert system development - Pattern recognition with uncertainty - Decision support with vague criteria ## Key Concepts ### Fuzzy Logic Basics ``` Classical: Temperature > 30 → Hot (true/false) Fuzzy: Temperature 28 → Hot (0.6), Warm (0.4) ``` ### Fuzzy Control Example ```python import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl # Define universes temperature = ctrl.Antecedent(np.arange(0, 41, 1), 'temperature') fan_speed = ctrl.Consequent(np.arange(0, 101, 1), 'fan_speed') # Membership functions temperature['cold'] = fuzz.trimf(temperature.universe, [0, 0, 20]) temperature['cool'] = fuzz.trimf(temperature.universe, [10, 25, 30]) temperature['hot'] = fuzz.trimf(temperature.universe, [25, 40, 40]) fan_speed['low'] = fuzz.trimf(fan_speed.universe, [0, 0, 50]) fan_speed['medium'] = fuzz.trimf(fan_speed.universe, [25, 50, 75]) fan_speed['high'] = fuzz.trimf(fan_speed.universe, [50, 100, 100]) # Rules rule1 = ctrl.Rule(temperature['cold'], fan_speed['low']) rule2 = ctrl.Rule(temperature['cool'], fan_speed['medium']) rule3 = ctrl.Rule(temperature['hot'], fan_speed['high']) # Control system fan_ctrl = ctrl.ControlSystem([rule1, rule2, rule3]) fan_sim = ctrl.ControlSystemSimulation(fan_ctrl) # Input and compute fan_sim.input['temperature'] = 28 fan_sim.compute() print(fan_sim.output['fan_speed']) ``` ### Key Concepts - **Membership functions**: Triangular, trapezoidal, Gaussian - **Fuzzy operators**: AND (min), OR (max), NOT (1-x) - **Defuzzification**: Centroid, bisector, MOM - **Inference**: Mamdani, Sugeno ### Applications - **Control**: AC, washing machines, autofocus - **Decision**: Credit scoring, medical diagnosis - **Pattern**: Image processing, recognition