Introduction to Fuzzy Logic and How It Is Used to Solve Engineering Problems
In Introduction to Fuzzy Logic and How It Is Used to Solve Engineering Problems , you'll learn ...
- What is “fuzzy logic” and its application in control systems and other areas of engineering
- Why fuzzy logic has not been widely adopted in the U.S. relative to other developed countries
- How probability and randomness relate to fuzzy logic
- How to define fuzzy data sets and convert crisp data into fuzzy data
Fuzzy logic is a form of probabilistic logic that deals with reasoning that is approximate rather than fixed and exact. Human beings process information through the fuzzy logic processes, yet computers cannot. Computers must deal with binary information and require crisp data to be converted to fuzzy data and fuzzy data sets in order to handle human-created algorithms.
The term "fuzzy logic" was introduced with the 1965 proposal of fuzzy set theory by Dr. Lotfi A. Zadeh. The field of “fuzzy logic” is incredibly broad. It is used in areas such as artificial intelligence, project management, product pricing models, sales forecasting, criminal identification, process control and signal processing.
E.H. Mamdani is credited with building the world's first fuzzy logic controller, after reading Dr. Zadeh's paper on the subject. Dr. Mamdani, London University, U.K., stated firmly and unequivocally that utilizing a fuzzy logic controller for speed control of a steam engine was much superior to controlling the engine by conventional mathematically based control systems and logic control hardware.
The term “fuzzy” carries different connotations in the English-speaking world than it does in other languages. This is one reason why international organizations have adapted and adopted these ideas faster than U.S. companies. One of the goals of this course is to help U.S. engineers understand and embrace the concept of “fuzziness” in system controls and other advanced decision making applications related to engineering.
Specific Knowledge or Skill Obtained
This course teaches the following specific knowledge and skills:
- The basic language of “fuzziness”
- Understanding data comprised of crisp information vs. fuzzy information
- How to create fuzzy data sets
- How fuzzy data sets can lead to better controllers and control systems
- How ‘defuzzing’ data can lead to crisp decisions
- The importance of incorporating fuzzy logic more sophisticated technologies, in order for computer the “think” and process information like humans
Certificate of Completion
You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 20 questions. PDH credits are not awarded until the course is completed and quiz is passed.
|This course is applicable to professional engineers in:|
|Alabama (P.E.)||Alaska (P.E.)||Arkansas (P.E.)|
|Delaware (P.E.)||Florida (P.E. Area of Practice)||Georgia (P.E.)|
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|Indiana (P.E.)||Iowa (P.E.)||Kansas (P.E.)|
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|Maryland (P.E.)||Michigan (P.E.)||Minnesota (P.E.)|
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|New Jersey (P.E.)||New Mexico (P.E.)||New York (P.E.)|
|North Carolina (P.E.)||North Dakota (P.E.)||Ohio (P.E. Self-Paced)|
|Oklahoma (P.E.)||Oregon (P.E.)||Pennsylvania (P.E.)|
|South Carolina (P.E.)||South Dakota (P.E.)||Tennessee (P.E.)|
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