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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
Overview
Preview a portion of this course before purchasing it.
Credit: 1 PDH
Length: 19 pages
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.
Learning Objectives
Upon completion of this course, participants will be able to:
- Define the basic terminology of fuzzy logic.
- Distinguish between crisp information and fuzzy information.
- Create fuzzy data sets.
- Explain how fuzzy data sets can improve controllers and control systems.
- Describe how defuzzification can produce crisp decisions.
- Explain the importance of incorporating fuzzy logic into more sophisticated technologies so computers can process information in ways that more closely resemble human reasoning.
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: | ||
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| Maine (P.E.) | Maryland (P.E.) | Michigan (P.E.) |
| Minnesota (P.E.) | Mississippi (P.E.) | Missouri (P.E.) |
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| New Hampshire (P.E.) | New Jersey (P.E.) | New Mexico (P.E.) |
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| Ohio (P.E. Self-Paced) | Oklahoma (P.E.) | Oregon (P.E.) |
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Preview a portion of this course before purchasing it.
Credit: 1 PDH
Length: 19 pages
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