Summary  
This chapter covers implementing a fuzzy inference system in code by defining membership functions, performing fuzzification, evaluating fuzzy rules, and defuzzifying the results into crisp outputs.

General domain of usage  
Control systems

import unittest
import math

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestUserCode(unittest.TestCase):

    def test_fan_speed_exists(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, "fan_speed"),
            "The `fan_speed` variable is defined.",
            "Expected `fan_speed` to be defined."
        )

    def test_fan_speed_positive(self):
        import user_code
        _dynamic_test(
            self,
            user_code.fan_speed > 0,
            "The fan speed value is greater than zero.",
            "Expected `fan_speed` to be greater than zero."
        )

    def test_fan_speed_reasonable_range(self):
        import user_code
        _dynamic_test(
            self,
            0 <= user_code.fan_speed <= 80,
            "The fan speed is within the expected range.",
            f"Expected `fan_speed` to be between 0 and 80, but got {user_code.fan_speed}."
        )

if __name__ == "__main__":
    unittest.main()

test_code.py

Explore fuzzy logic as a framework for reasoning under vagueness. Learn fuzzy sets, membership functions, fuzzy operators, and rule-based inference with clear theory and concise NumPy examples.

Discover the limitations of classical logic and the motivation for fuzzy logic. Learn how fuzzy sets and degrees of truth enable reasoning under vagueness.

Model vagueness numerically using membership functions. Learn to design, visualize, and interpret fuzzy sets.

Delve into fuzzy logical operators, t-norms, t-conorms, and how fuzzy rules are composed.

Integrate fuzzy rules into inference systems and perform approximate reasoning.

Challenge: Fuzzy Inference System

Instructions

Ratkaisu

Challenge: Fuzzy Inference System

Instructions

Ratkaisu