Fuzzy logic and neural networks


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Fuzzy logic and neural networks

  1. 1. By. Sajid Hussain Qazi MUCET, Khairpur10/31/2012 1
  2. 2. INTRODUCTION Fuzzy logic has rapidly become one of the most successful of todays technologies for developing sophisticated control systems. The reason for which is very simple. Fuzzy logic addresses such applications perfectly as it resembles human decision making with an ability to generate precise solutions from certain or approximate information. It fills an important gap in engineering design methods left vacant by purely mathematical approaches (e.g. linear control design), and purely logic-based approaches (e.g. expert systems) in system design.10/31/2012 2
  3. 3.  While other approaches require accurate equations to model real-world behaviors, fuzzy design can accommodate the ambiguities of real-world human language and logic.  It provides both an intuitive method for describing systems in human terms and automates the conversion of those system specifications into effective models.10/31/2012 3
  4. 4. HISTORY:- Lotfi A. Zadeh, a professor of UC Berkeley in California, soon to be known as the founder of fuzzy logic observed that conventional computer logic was incapable of manipulating data representing subjective or vague human ideas such as "an atractive person" . Fuzzy logic, hence was designed to allow computers to determine the distinctions among data with shades of gray, similar to the process of human reasoning.10/31/2012 4
  5. 5. Fuzzy Sets A paradigm is a set of rules and regulations which defines boundaries and tells us what to do to be successful in solving problems within these boundaries. For example the use of transistors instead of vacuum tubes is a paradigm shift - likewise the development of Fuzzy Set Theory from conventional bivalent set theory is a paradigm shift. Bivalent Set Theory can be somewhat limiting if we wish to describe a humanistic problem mathematically.10/31/2012 5
  6. 6. What does it offer? The first applications of fuzzy theory were primarily industrial, such as process control for cement kilns. Since then, the applications of Fuzzy Logic technology have virtually exploded, affecting things we use everyday. Take for example, the fuzzy washing machine . A load of clothes in it and press start, and the machine begins to turn, automatically choosing the best cycle. The fuzzy microwave, Place chili, potatoes, or etc in a fuzzy microwave and push single button, and it cooks for the right time at the proper temperature. The fuzzy car, maneuvers itself by following simple verbal instructions from its driver. It can even stop itself when there is an obstacle immediately ahead10/31/2012 6 using sensors.
  7. 7. How do fuzzy sets differ from classicalsets? In classical set theory we assume that all sets are well-defined (or crisp),  CLASSICAL SETS  The set of people that can run a mile in 4 minutes or less.  The set of children under age seven that weigh more than 1oo pounds.  FUZZY SETS  The set of fast runners.  The set of overweight children.10/31/2012 7
  8. 8. FUZZY CONTROL:- Fuzzy control, which directly uses fuzzy rules is the most important application in fuzzy theory. Using a procedure originated by Ebrahim Mamdani in the late 70s, three steps are taken to create a fuzzy controlled machine: 1) Fuzzification(Using membership functions to graphically describe a situation) 2) Rule evaluation(Application of fuzzy rules) 3) Defuzzification(Obtaining the crisp or actual results)10/31/2012 8
  9. 9. WHY FUZZY CONTROL? Fuzzy Logic is a technique to embody human like thinking into a control system. A fuzzy controller is designed to emulate human deductive thinking, that is, the process people use to infer conclusions from what they know. Traditional control approach requires formal modeling of the physical reality. Fuzzy logic is widely used in machine control.10/31/2012 9
  10. 10. WHY FUZZY CONTROL? Although genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans.10/31/2012 10
  11. 11. LITTLE MORE ON FUZZY CONTROL:- Fuzzy controllers are very simple conceptually. They consist of an input stage, a processing stage, and an output stage. The input stage maps sensor or other inputs, such as switches, thumbwheels, and so on, to the appropriate membership functions and truth values. The processing stage invokes each appropriate rule and generates a result for each, then combines the results of the rules. Finally, the output stage converts the combined result back into a specific control output value.10/31/2012 11
  12. 12. How far can fuzzy logic go??? It can appear almost anyplace where computers and modern control theory are overly precise as well as in tasks requiring delicate human intuition and experience-based knowledge. What does the future hold? Computers that understand and respond to normal human language. Machines that write interesting novels and screenplays in a selected style , such as Hemingways. Molecule-sized soldiers of health that will roam the blood-stream, killing cancer cells and slowing the10/31/2012 12 aging process.
  13. 13.  Hence, it can be seen that with the enormous research currently being done in Japan and many other countries whose eyes have opened, the future of fuzzy logic is undetermined. There is no limit to where it can go. The future is bright. The future is fuzzy.10/31/2012 13
  14. 14. FUZZY LOGIC IN CONTROLSYSTEMS Fuzzy Logic provides a more efficient and resourceful way to solve Control Systems. Some Examples  Temperature Controller  Anti – Lock Break System ( ABS )
  15. 15. Artificial Neural Networks  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.
  16. 16. ANNs ANNs are able to learn from examples. Function approximators. Solutions not always correct. ANNs are able to generalize the acquired knowledge.
  17. 17. Neural Networks
  18. 18. 10/31/2012 18