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  3. 3. HISTORY OF FUZZY LOGIC • 1965 - Fuzzy Sets ( Lofti Zadeh, seminar) • 1966 - Fuzzy Logic ( P. Marinos, Bell Labs) • 1972 - Fuzzy Measure ( M. Sugeno, TIT) • 1974 - Fuzzy Logic Control (E.H. Mamdani) • 1980 - Control of Cement Kiln (F.L. Smidt, Denmatk) • 1987 - Sendai Subway Train Experiment ( Hitachi) • 1988 - Stock Trading Expert System (Yamaichi) • 1989 - LIFE ( Lab for International Fuzzy Eng)
  4. 4. OBJECTIVE • TO MAKE INDIAN INDUSTRIES CAST EFFECTIVE • FMS is considered to be highly flexible and highly integrated system , but they cost heavy and most of the Indian industries can not afford this. So it is relevant to find a solution for Indian industries which could offer cost efficient ways to achieve this.
  5. 5. MOTIVATION • The machine learning technique in the field of artificial intelligence • Approaches used include fuzzy logic approaches, artificial neural networks, and the application of adaptive-networkbased fuzzy inference systems (ANFIS) • Fuzzy logic approaches easily deal with uncertain and incomplete information • Approaches in scheduling of flexible manufacturing systems increased
  7. 7. FMS(FLEXIBLE MANUFACTURING SYSTEM) • A manufacturing system in which there is some amount of flexibility that allows the system to react in the case of changes, whether predicted or unpredicted • Comes in the middle of the 1960s • Philosophically, FMS incorporates a system view of manufacturing • We must become managers of technology not merely users of technology by Peter Drucker • Today flexibility means to produce reasonably priced customized products of high quality that can be quickly delivered to customers
  8. 8. BASIC COMPONENTS OF FMS • Workstations • Material handling and storage system • Computer control system • People are required to manage and operate the system.
  9. 9. AUTOMATED MANUFACTURING CELL Machine Tool Parts Carousel Robot Machine Work table
  10. 10. WORKSTATIONS • Load/Unload Stations - Physical interface: FMS and factory • Machining Stations - Most common is the CNC machining centre • Other Processing Stations – sheet-metal fabrication, forging • Assembly - Industrial robots, component placement machines • Other Stations and Equipment -inspection stations, cleaning stations, central coolant delivery and chip removal systems
  11. 11. ADVANTAGES OF FMS • Increased machine utilization • Fewer machines required • Reduction in factory floor space required • Greater responsiveness to change • Reduced inventory requirements • Lower manufacturing lead times • Reduced direct labor requirements and higher labor productivity • Opportunity for unattended production
  12. 12. DISADVANTAGES OF FMS • Substantial pre-planning activity • Expensive, costing millions of dollars • Sophisticated manufacturing systems • Limited ability to adapt to changes in product or product mix • Technological problems of exact component positioning and precise timing necessary to process a component
  13. 13. ARTIFICIAL INTELLIGENCE • “AI is the activity of providing such machines as computers with the ability to display behaviours that would be regarded as intelligent if it were observed in humans” (R. McLeod) • “AI is the study of agents that exist in an environment, perceive and act.” (S. Russel and P. Norvig)
  14. 14. ARTIFICIAL NEURAL NETWORK • Computational models that try to emulate the structure of the human brain wishing to reproduce at least some of its flexibility and power. • ANN consist of many simple computing elements – usually simple nonlinear summing operations – highly connected by links of varying strength • ANNs are able to learn from examples • Function approximations • Solutions not always correct • ANNs are able to generalize the acquired knowledge
  15. 15. TRAINING • Weight values change during the training process • Values are presented at the inputs and outputs are compared to the desired values. • Wrong outputs cause weights to change in order to reduce the error • Process is repeated with different inputs till the ANN is able to give the correct answers • Hopefully the ANN will be able to give the correct answer even to inputs that were not trained.
  16. 16. FUZZY LOGIC : AN IDEA 1.0
  17. 17. FUZZY LOGIC • Introduced by Lofti Zadeh (1965) • It is a powerful problem-solving methodology • • Builds on a set of user-supplied human language rules It deals with uncertainty and ambiguous criteria or values • Example: “the weather outside is cold” • • • • but, how cold is actually the coldness you described? What do you mean by „cold‟ here? As you can see a particular temperature is cold to one person but it is not to another It depends on one‟s relative definition of the said term
  18. 18. FUZZY SETS • Formal definition: • A fuzzy set A in X is expressed as a set of ordered pairs: A Fuzzy set {( x, A ( x ))| x Membership function (MF) X} Universe or universe of discourse A fuzzy set is totally characterized by a membership function (MF).
  19. 19. • Most natural language is bounded with vague and imprecise concepts • Example: • • “The student is intelligent” • • “He is quite tall” “Today is a very hot day” These statements are difficult to translate into more precise language • Fuzzy logic was introduced to design systems that can demonstrate human-like reasoning capability to understand such vague terms
  20. 20. DIFFERENCES BETWEEN FUZZY LOGIC AND CRISP LOGIC • CRISP LOGIC • • • • • • • YES or NO TRUE or FALSE 1 or 0 Crisp Sets she is 18 years old • man 1.6m tall FUZZY LOGIC • precise properties Full membership • • Partial membership • • • • Imprecise properties YES ---> NO TRUE ---> FALSE 1 ---> 0 Fuzzy Sets • she is about 18 years old • man about 1.6m tall
  21. 21. HOW DOES FUZZY LOGIC RESEMBLES HUMAN INTELLIGENCE? • It can handle at certain level of imprecision and uncertainty • By clustering & classification • • focusing on each part with rank of importance and alternatives to solve • • dividing the scenario/problems into parts combining the parts to as an integrated whole It reflects some forms of the human reasoning process by • Setting hypothetical rules • Performing inferencing • Performing logic reasoning on the rules
  22. 22. METHODOLOGY EXAMPLE: FUZZY INFERENCE • Inputs to a fuzzy system can be: – fuzzy, e.g. (Score = Moderate), defined by membership functions; – exact, e.g.: (Score = 190); defined by crisp values • Outputs from a fuzzy system can be: – fuzzy, i.e. a whole membership function. – exact, i.e. a single value is produced
  23. 23. EXAMPLE: FUZZY INFERENCE • Inputs to a fuzzy system can be: – fuzzy, e.g. (Score = Moderate), defined by membership functions; – exact, e.g.: (Score = 190); defined by crisp values • Outputs from a fuzzy system can be: – fuzzy, i.e. a whole membership function. – exact, i.e. a single value is produced
  24. 24. WHAT IS THE DIFFERENCE BETWEEN CLASSICAL AND FUZZY RULES? Consider the rules in fuzzy form, as follows: Rule 1 Rule 2 IF driving_speed is fast IF driving_speed is slow THEN stop_distance is long THEN stop_distance is short In fuzzy rules, the linguistic variable speed can have the range between 0 and 220 km/h, but the range includes fuzzy sets, such as slow, medium, fast. Linguistic variable stop_distance can take either value: long or short. The universe of discourse of the linguistic variable stop_distance can be between 0 and 300m and may include such fuzzy sets as short, medium, and long.
  25. 25. FUZZY LOGIC METHODOLOGY • Set the boundaries between two values(cold and hot) which will show the degrees of temperature • A sample set of rules • IF temperature is cold THEN set fan speed to zero • IF temperature is cool THEN set fan speed to low • IF temperature is warm THEN set fan speed to medium • IF temperature is hot THEN set fan speed to high
  26. 26. DESIGN A SET OF FUZZY RULES FOR AN ELECTRICAL WASHING MACHINE IF Load_Weight is heavy THEN set Water_Amount to full IF Load_Weight is not_so_heavy THEN set Water_Amount to three_quarter IF Load_Weight is not_so_light THEN set Water_Amount to half IF Load_Weight is light THEN set Water_Amount to quarter Or IF Load Weight is heavy THEN set Water Amount to maximum IF Load Weight is medium THEN set Water Amount to regular IF Load Weight is light THEN set Water Amount to minimum
  27. 27. ALTERNATIVE NOTATION • A fuzzy set A can be alternatively denoted as follows: X is discrete X is continuous A A ( xi ) / xi xi X A A (x) / x X Note that S and integral signs stand for the union of membership grades; “/” stands for a marker and does not imply division.
  28. 28. FUZZY LOGIC OPERATIONS • Fuzzy Logic Operators are used to write logic combinations between fuzzy notions (i.e. to perform computations on degree of membership) • Zadeh operators 1. Intersection: The logic operator corresponding to the intersection of sets is AND µ(A AND B) = MIN (µA,µB) 2. Union: The logic operator corresponding to the union of sets is OR µ(A OR B) = MAX (µA,µB) 3. Negation: The logic operator corresponding to the complement of a set is the negation µ(NOTA) = 1-µA
  30. 30. IMPLEMENTATION PLAN task problem search problem identification litreture survey learning of anfis data collection experimentation analysis result of inference report writing final submission aug sept oct nov dec jan feb mar april may june
  31. 31. EXPECTED OUTCOME • Fuzzy Logic Decision Making is used in many applications • Implemented using fuzzy sets operation(if , then , else statements & logical operators) • Resembles human decision making with its ability to work from approximate data and find a precise solutions • Cost effective FMS(Flexible Manufacturing System) system may be dsign
  32. 32. SOME SNAP SHOTS training the data in anfis editor
  33. 33. SOME SNAP SHOTS structure of the trained data
  34. 34. RULES of the fuzzy-logic
  35. 35. SURFACE of the fuzzy-logic