Description of the Course:
The role of Fuzzy Logic in undestanding of human cognition and our ability to monitor and control complex systems and build engineering systems that simulate human decision making in uncertain and imprecise environments is studied. The axioms of Fuzzy Set Theory are compared to those of Ordinary Set Theory and Fuzzy Logic is compared to Boolean Logic. Coupled probability-possibility theory within a body of evidence and fuzzy information granules, is presented. The concept of performance level is compared to reliability. Relevant computing paradigms are considered: Expert Systems, Anticipatory Systems, Insightful Algorithms, and Neural Networks. Applications of Artificial Intelligence (AI) based on symbol manipulation and first-order logic, such as production-rule systems, expert systems and natural language interfaces are first analyzed. Extension to systems that require extensive numerical computations such as neural network theory is next considered in more detail. The ability to solve a maltitude of engineering problems ranging from process control and signal processing to fault diagnosis and system optimization using these new approaches is studied.

Prerequisites:
Adequate mathematical background: Calculus: Math 280 or equivalent, Elements of Probability: Math 361/Stat 351 or ECE 313 or GE 289 or IE 238 or ME 347 or CSE 362/NE 357; junior-senior or graduate standing, and computer programming experience: CS 101 or equivalent, or consent of instructor.

Credit: 3 hours, or 3/4 unit or 1 unit with term project.

Format: Lectures and computer assignments.

Semester: Spring semester.