1 / 44

Fuzzy Expert System Fuzzy Logic

Fuzzy Expert System Fuzzy Logic. دكترمحسن كاهاني http://www.um.ac.ir/~kahani/. Introduction. Experts rely on common sense when they solve problems. How can we represent expert knowledge that uses vague and ambiguous terms in a computer?

sonya-mcpherson
Download Presentation

Fuzzy Expert System Fuzzy Logic

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fuzzy Expert SystemFuzzy Logic دكترمحسن كاهاني http://www.um.ac.ir/~kahani/

  2. Introduction • Experts rely on common sense when they solve problems. • How can we represent expert knowledge that uses vague and ambiguous terms in a computer? • Fuzzy logic is not logic that is fuzzy, but logic that is used to describe fuzziness. • Fuzzy logic is the theory of fuzzy sets, sets that calibrate vagueness. • Fuzzy logic is based on the idea that all things admit of degrees. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  3. Fuzzy Logic • Boolean logic uses sharp distinctions. • Fuzzy logic reflects how people think. It attempts to model our sense of words, our decision making and our common sense. As a result, it is leading to new, more human, intelligent systems. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  4. Fuzzy Logic Histroty • Fuzzy, or multi-valued logic was introduced in the 1930s by Jan Lukasiewicz, a Polish philosopher. This work led to an inexact reasoning technique often called possibility theory. • Later, in 1937, Max Black published a paper called “Vagueness: an exercise in logical analysis”. In this paper, he argued that a continuum implies degrees. • In 1965 Lotfi Zadeh, published his famous paper “Fuzzy sets”. • Zadeh extended possibility theory into a formal system of mathematical logic. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  5. Why fuzzy? • As Zadeh said, the term is concrete, immediate and descriptive. Why logic? • Fuzziness rests on fuzzy set theory, and fuzzy logic is just a small part of that theory. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  6. Definition • Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership. • Unlike two-valued Boolean logic, fuzzy logic is multi-valued. • It deals with degrees of membership and degrees of truth. • Fuzzy logic uses the continuum of logical values between 0 (completely false) and 1 (completely true). سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  7. Range of logical values in Boolean and fuzzy logic سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  8. Fuzzy sets • The concept of a set is fundamental to mathematics. • However, our own language is also the supreme expression of sets. For example, car indicates the set of cars. When we say a car, we mean one out of the set of cars. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  9. Tall men example سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  10. Crisp and fuzzy sets of “tall men” سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  11. A fuzzy set is a set with fuzzy boundaries • The x-axis represents the universe of discourse • The y-axis represents the membership value of the fuzzy set. • In classical set theory, crisp set A of X is defined as fA(x): X → {0, 1},where • In fuzzy theory, fuzzy set A of universe X is defined μA(x): X → [0, 1], where μA(x) = 1 if x is totally in A; μA(x) = 0 if x is not in A; 0 < μA(x) < 1 if x is partly in A. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  12. fuzzy set representation • First, we determine the membership functions. In our “tall men” example, we can obtain fuzzy sets of tall, short and average men. • The universe of discourse − the men’s heights − consists of three sets: short, average and tall men. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  13. Crisp and fuzzy sets سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  14. Representation of crisp and fuzzy subsets • Typical functions : sigmoid, gaussian and pi. • However, these functions increase the time of computation. Therefore, in practice, most applications use linear fit functions. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  15. “tall” in NBA Membership Functions (MFs) • Characteristics of MFs: • Subjective measures • Not probability functions “tall” in Asia MFs .8 “tall” in the US .5 .1 5’10’’ Heights سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  16. Fuzzy Sets • Formal definition: A fuzzy set A in X is expressed as a set of ordered pairs: Membership function (MF) Universe or universe of discourse Fuzzy set A fuzzy set is totally characterized by a membership function (MF). سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  17. Fuzzy Sets with Discrete Universes • Fuzzy set C = “desirable city to live in” X = {SF, Boston, LA} (discrete and nonordered) C = {(SF, 0.9), (Boston, 0.8), (LA, 0.6)} • Fuzzy set A = “sensible number of children” X = {0, 1, 2, 3, 4, 5, 6} (discrete universe) A = {(0, .1), (1, .3), (2, .7), (3, 1), (4, .6), (5, .2), (6, .1)} سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  18. Fuzzy Sets with Cont. Universes • Fuzzy set B = “about 50 years old” X = Set of positive real numbers (continuous) B = {(x, mB(x)) | x in X} سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  19. Alternative Notation • A fuzzy set A can be alternatively denoted as follows: X is discrete X is continuous Note that S and integral signs stand for the union of membership grades; “/” stands for a marker and does not imply division. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  20. Fuzzy Partition • Fuzzy partitions formed by the linguistic values “young”, “middle aged”, and “old”: سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  21. MF Terminology MF 1 .5 a 0 Core X Crossover points a - cut Support سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  22. MF Formulation Triangular MF: Trapezoidal MF: Gaussian MF: Generalized bell MF: سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  23. MF Formulation سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  24. Linguistic variables and hedges • At the root of fuzzy set theory lies the idea of linguistic variables. • Alinguistic variable is a fuzzy variable. For example, the statement “John is tall” implies that the linguistic variable John takes the linguistic value tall. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  25. Example • In fuzzy expert systems, linguistic variables are used in fuzzy rules. For example: IF wind is strong THEN sailing is good IF project_duration is long THEN completion_risk is high IF speed is slow THEN stopping_distance is short سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  26. Hedge • A linguistic variable carries with it the concept of fuzzy set qualifiers, called hedges. • Hedges are terms that modify the shape of fuzzy sets. They include adverbs such as very, somewhat, quite, more or less and slightly. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  27. Fuzzy sets with the hedge very سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  28. Representation of hedges سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  29. Representation of hedges (cont.) سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  30. Operations of fuzzy sets • The classical set theory developed in the late 19th century by Georg Cantor describes how crisp sets can interact. These interactions are called operations. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  31. Cantor’s sets سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  32. Complement Crisp Sets: Who does not belong to the set? Fuzzy Sets: How much do elements not belong to the set? • The complement of a set is an opposite of this set. μØA(x) = 1 − μA(x) سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  33. Containment Crisp Sets: Which sets belong to which other sets? Fuzzy Sets: Which sets belong to other sets? • A set can contain other sets. The smaller set is called subset. • In crisp sets, all elements of a subset entirely belong to a larger set. • In fuzzy sets, each element can belong less to the subset than to the larger set. Elements of the fuzzy subset have smaller memberships in it than in the larger set. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  34. Intersection Crisp Sets: Which element belongs to both sets? Fuzzy Sets: How much of the element is in both sets? • In classical set theory, an intersection between two sets contains the elements shared by these sets • In fuzzy sets, an element may partly belong to both sets with different memberships. A fuzzy intersection is the lower membership in both sets of each element. μA∩B(x) = min [μA(x), μB(x)] = μA(x) ∩ μB(x) where xÎX سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  35. Union Crisp Sets: Which element belongs to either set? Fuzzy Sets: How much of the element is in either set? • The union of two crisp sets consists of every element that falls into either set. • In fuzzy sets, the union is the reverse of the intersection. That is, the union is the largest membership value of the element in either set. μAÈB(x) = max [μA(x), μB(x)] = μA(x) È μB(x) where xÎX سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  36. Operations of fuzzy sets سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  37. Fuzzy rules • In 1973, Lotfi Zadeh published his second most influential paper. This paper outlined a new approach to analysis of complex systems, in which Zadeh suggested capturing human knowledge in fuzzy rules. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  38. What is a fuzzy rule? • A fuzzy rule can be defined as a conditional statement in the form: IF x is A THEN y is B • where x and y are linguistic variables; and A and B are linguistic values determined by fuzzy sets on the universe of discourses X and Y, respectively. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  39. classical vs. fuzzy rules? • A classical IF-THEN rule uses binary logic Rule: 1 IF speed is > 100 THEN stopping_distance is long Rule: 2 IF speed is < 40 THEN stopping_distance is short • Representing the stopping distance rules in a fuzzy form: Rule: 1 IF speed is fast THEN stopping_distance is long Rule: 2 IF speed is slow THEN stopping_distance is short سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  40. Fuzzy Rules • Fuzzy rules relate fuzzy sets. • In a fuzzy system, all rules fire to some extent, or in other words they fire partially. • If the antecedent is true to some degree of membership, then the consequent is also true to that same degree سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  41. Fuzzy sets of tall and heavy men • These fuzzy sets provide the basis for a weight estimation model. The model is based on a relationship between a man’s height and his weight: IF height is tall THEN weight is heavy سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  42. monotonic selection • The value of the output or a truth membership grade of the rule consequent can be estimated directly from a corresponding truth membership grade in the antecedent. This form of fuzzy inference uses a method called monotonic selection. سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  43. Fuzzy Rule • A fuzzy rule can have multiple antecedents, for example: IF project_duration is long AND project_staffing is large AND project_funding is inadequate THEN risk is high IF service is excellent OR food is delicious THEN tip is generous سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

  44. Fuzzy Rule • The consequent of a fuzzy rule can also include multiple parts, for instance: IF temperature is hot THEN hot_water is reduced; cold_water is increased سيستمهاي خبره و مهندسي دانش-دكتر كاهاني

More Related