t-Norms and t-Conorms
In fuzzy logic, you often need to combine two or more fuzzy truth values to model interactions between vague concepts. The standard fuzzy operations you have seen so far — such as fuzzy AND (minimum) and fuzzy OR (maximum) — are just one way to do this. More generally, these operations can be formalized using t-norms (triangular norms) and t-conorms (also called s-norms).
A t-norm is a mathematical function that generalizes the notion of fuzzy intersection or logical AND. It takes two values in the range [0,1] and produces another value in [0,1], representing the degree to which both conditions are satisfied. Similarly, a t-conorm generalizes the fuzzy union or logical OR, combining two fuzzy truth values into a single value in [0,1] that reflects the degree to which at least one condition is satisfied.
These operations are crucial in fuzzy systems, as they determine how rules and conditions interact when you model real-world systems with overlapping, ambiguous properties.
Several t-norms and t-conorms are widely used:
Common t-norms:
- Minimum t-norm: returns the smaller of the two values; this is the standard fuzzy AND;
- Product t-norm: returns the product of the two values; this models the joint probability of independent fuzzy events.
Common t-conorms:
- Maximum t-conorm: returns the larger of the two values; this is the standard fuzzy OR;
- Probabilistic sum t-conorm: returns a+b−a∗b, which is the probability that at least one of two independent fuzzy events occurs.
Choosing different t-norms or t-conorms allows you to tune how strictly or loosely fuzzy conditions are combined, which can make your fuzzy system more flexible or more conservative depending on your needs.
123456789101112131415161718192021import numpy as np # Example fuzzy sets (membership values) # Suppose these represent degrees of "tall" and "athletic" for a group of people tall = np.array([0.2, 0.5, 0.8, 1.0]) athletic = np.array([0.7, 0.4, 0.9, 0.3]) # t-norms (fuzzy AND) min_tnorm = np.minimum(tall, athletic) # Minimum t-norm product_tnorm = tall * athletic # Product t-norm # t-conorms (fuzzy OR) max_tconorm = np.maximum(tall, athletic) # Maximum t-conorm prob_sum_tconorm = tall + athletic - tall * athletic # Probabilistic sum t-conorm print("Tall:", tall) print("Athletic:", athletic) print("Min t-norm (AND):", min_tnorm) print("Product t-norm (AND):", product_tnorm) print("Max t-conorm (OR):", max_tconorm) print("Probabilistic sum t-conorm (OR):", prob_sum_tconorm)
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In fuzzy logic, you often need to combine two or more fuzzy truth values to model interactions between vague concepts. The standard fuzzy operations you have seen so far — such as fuzzy AND (minimum) and fuzzy OR (maximum) — are just one way to do this. More generally, these operations can be formalized using t-norms (triangular norms) and t-conorms (also called s-norms).
A t-norm is a mathematical function that generalizes the notion of fuzzy intersection or logical AND. It takes two values in the range [0,1] and produces another value in [0,1], representing the degree to which both conditions are satisfied. Similarly, a t-conorm generalizes the fuzzy union or logical OR, combining two fuzzy truth values into a single value in [0,1] that reflects the degree to which at least one condition is satisfied.
These operations are crucial in fuzzy systems, as they determine how rules and conditions interact when you model real-world systems with overlapping, ambiguous properties.
Several t-norms and t-conorms are widely used:
Common t-norms:
- Minimum t-norm: returns the smaller of the two values; this is the standard fuzzy AND;
- Product t-norm: returns the product of the two values; this models the joint probability of independent fuzzy events.
Common t-conorms:
- Maximum t-conorm: returns the larger of the two values; this is the standard fuzzy OR;
- Probabilistic sum t-conorm: returns a+b−a∗b, which is the probability that at least one of two independent fuzzy events occurs.
Choosing different t-norms or t-conorms allows you to tune how strictly or loosely fuzzy conditions are combined, which can make your fuzzy system more flexible or more conservative depending on your needs.
123456789101112131415161718192021import numpy as np # Example fuzzy sets (membership values) # Suppose these represent degrees of "tall" and "athletic" for a group of people tall = np.array([0.2, 0.5, 0.8, 1.0]) athletic = np.array([0.7, 0.4, 0.9, 0.3]) # t-norms (fuzzy AND) min_tnorm = np.minimum(tall, athletic) # Minimum t-norm product_tnorm = tall * athletic # Product t-norm # t-conorms (fuzzy OR) max_tconorm = np.maximum(tall, athletic) # Maximum t-conorm prob_sum_tconorm = tall + athletic - tall * athletic # Probabilistic sum t-conorm print("Tall:", tall) print("Athletic:", athletic) print("Min t-norm (AND):", min_tnorm) print("Product t-norm (AND):", product_tnorm) print("Max t-conorm (OR):", max_tconorm) print("Probabilistic sum t-conorm (OR):", prob_sum_tconorm)
Merci pour vos commentaires !
