Dr. Unnikrishnan P.C. Professor, EEE EE368 Soft Computing Dr. Unnikrishnan P.C. Professor, EEE

Module III Hybrid Intelligent Systems Fuzzy Expert Systems

Hybrid Intelligent Systems: Neural expert systems and Neuro-fuzzy systems Introduction  Neural expert systems  Fuzzy expert Systems Neuro-Fuzzy systems ANFIS: Adaptive Neuro-Fuzzy Inference System

Fuzzy Rule-based Expert System

Fuzzy Rule-based Expert System

Fuzzy Rules In 1973, Lotfi Zadeh published his second most influential paper. He suggested capturing human knowledge in fuzzy rules. 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; A and B are linguistic values determined by fuzzy sets on the universe of discourses X and Y, respectively. Antecedent (or condition): x is A Consequent (or conclusion): y is B

Classical vs. Fuzzy Rules Classical rule: Rule 1: Rule 2: IF speed is > 100 (km/h) IF speed is < 40 (km/h) THEN stopping_distance is > 100m THEN stopping_distance is < 40m Fuzzy rule: IF speed is fast IF speed is slow THEN stopping_distance is long THEN stopping_distance is short Fuzzy rules relate fuzzy sets. In a fuzzy system, all rules fire partially.

Firing Fuzzy Rules IF height is tall THEN weight is heavy

Firing Fuzzy Rules If the antecedent is true to some degree of membership, then the consequent is also true to that same degree. This form of fuzzy inference is called monotonic selection.

Firing Fuzzy Rules 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

Firing Fuzzy Rules 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 Solutions: Mamdani or Sugeno approaches

Fuzzy Inference Techniques Mamdani The most commonly used fuzzy inference technique He built one of the first fuzzy systems to control a steam engine He applied a set of fuzzy rules supplied by experienced human operators. E. Mamdani, “Application of fuzzy algorithms for control of simple dynamic plant” (Proc. IEE, Vol.121, No. 12, pp. 1585-1588, 1974) E. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, (Int. J. of Man-Machine Studies, Vol.7, No.1, pp. 1- 13, 1975)

Fuzzy Inference Techniques Sugeno The ‘Zadeh of Japan’ Sugeno, Michio. ”Industrial applications of fuzzy control,” Elsevier Science Inc., 1985.

Mamdani Fuzzy Inference Four steps: Fuzzification of the input variables Rule evaluation (inference) Aggregation of the rule outputs (composition) Defuzzification.

Mamdani Fuzzy Inference We examine a simple two-input one-output problem that includes three rules: Rule: 1 Rule: 1 IF x is A3 IF project_funding is adequate OR y is B1 OR project_staffing is small THEN z is C1 THEN risk is low Rule: 2 Rule: 2 IF x is A2 IF project_funding is marginal AND y is B2 AND project_staffing is large THEN z is C2 THEN risk is normal Rule: 3 Rule: 3 IF x is A1 IF project_funding is inadequate THEN z is C3 THEN risk is high

Step 1: Fuzzification Take the crisp inputs, x1 and y1 (project funding and project staffing; e.g. x1=2million, y1:10 persons), and determine the degree to which these inputs belong to each of the appropriate fuzzy sets. A1: Inadequate, A2: Marginal, A3: Adequate B1: Small, B2: Large

Step 2: Rule Evaluation Take the fuzzified inputs, (x=A1) = 0.5, (x=A2) = 0.2, (y=B1) = 0.1 and (y=B2) = 0.7, and apply them to the antecedents of the fuzzy rules. If a given fuzzy rule has multiple antecedents, the fuzzy operator (AND or OR) is used to obtain a single number that represents the result of the antecedent evaluation. This number (the truth value) is then applied to the consequent membership function. (monotonic selection)

Step 2: Rule Evaluation

Step 2: Rule Evaluation How the result of the antecedent evaluation can be applied to the membership function of the consequent? Clipping (alpha-cut) Cut the consequent membership function at the level of the antecedent truth. losing some information. it is often preferred because it involves less complex and faster mathematics Scaling offers a better approach for preserving the original shape of the fuzzy set. Multiplying all its membership degrees by the truth value of the rule antecedent. It loses less information

Step 2: Rule Evaluation clipping scaling

Step 3: Aggregation of the rule outputs The process of unification of the outputs of all rules. Combining with MAX operator

Step 4: Defuzzification Input: the aggregate output fuzzy set Output: a single number The most popular method: Centroid technique. It finds the point where a vertical line would slice the aggregate set into two equal masses. Mathematically, it’s the center of gravity (COG)

Step 4: Defuzzification A reasonable estimate can be obtained by calculating it over a sample of points.

Step 4: Defuzzification

Mamdani Inference Technique

Sugeno Fuzzy Inference In Mamdani-style inference, to find the centroid, an integration across a continuously varying function is required. no computationally efficient! Michio Sugeno suggested to use a single spike, a singleton Fuzzy Rules in zero-order Sugeno fuzzy model: IF x is A AND y is B THEN z is k where k is a constant.

Sugeno Rule Evaluation

Sugeno Aggregation of the Rule Outputs IF project_funding is adequate OR project_staffing is small, THEN risk is k1 Rule 2: IF project_funding is marginal AND project_staffing is large, THEN risk is k2 Rule 3: IF project_funding is inadequate, THEN risk is k3

Sugeno Defuzzification Weighted Average (WA) Suppose: k1=20, k2=50, k3=80

Sugeno Inference Technique

Mamdani or Sugeno? Mamdani widely accepted for capturing expert knowledge more intuitive, more human-like manner a substantial computational burden Sugeno computationally effective works well with optimization and adaptive techniques e.g. control problems, particularly for dynamic nonlinear systems.

Advantages and Problems of Fuzzy Logic general theory of uncertainty wide applicability, many practical applications natural use of vague and imprecise concepts helpful for commonsense reasoning, explanation Problems membership functions can be difficult to find multiple ways for combining evidence problems with long inference chains