CET4xx - Neural & Fuzzy Logic Systems

CET4xx - Neural & Fuzzy Logic Systems
J. Sumey 29-Oct-98
This upper-level course explores areas of alternative computer engineering methodologies and
provides roughly equal coverage of neural networks and fuzzy logic systems. Neural-based logic
systems attempt to solve problems by approximating the operation of the human nervous system
instead of the procedural methods typically taught in computer science. Neural networks are
particularly useful in pattern matching type applications. Fuzzy logic systems also seek to solve
problems, such as the inverted pendulum problem, from a more abstract, non-procedural
approach whereby results are determined by “strength of truth” of membership functions. The
components of a fuzzy system including input fuzzification, inference engine and rule set, and
output defuzzification are examined. Both neural and fuzzy methodologies are being applied
more frequently as computers are being used to solve larger and more complicated problems as
well as in many embedded systems. Being a laboratory course, the student will complete a
number of hands-on projects involving software and/or microprocessor-based systems that
demonstrate practical, real-world applications of neural and fuzzy logic systems.