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Neural Computing in Engineering


Credit Hours:


Learning Objective:


The course presents the mathematical fundamentals of computing with neural networks and a survey of engineering applications. Computational metaphors from biological neurons serve as the basis for artificial neural networks modeling complex, non-linear and ill-posed problems. Applications emphasize the engineering utilization of neural computing to diagnostics, control, safety and decision-making problems.

Topics Covered:

  1. Basics
  2. Backpropagation and Related Training Algorithms
  3. Feedback and Other Special Neural Networks
  4. Dynamic Neural Networks and Control Systems
  5. Practical Aspects of Using Neural Networks
  6. Advanced Topics


Applied / Theory:

50 / 50


A number of HW sets (8-10) will be given during the semester. These are typically problems from the book and should help with developing some computing skills


Develop a project (preferably as a group effort) to (ideally) the level of research publication. It involves writing a report and doing a class presentation


A midterm exam (take-home) will be given to review and sharpen your analytical skills in fuzzy mathematics


Tsoukalas, L.H., Uhrig, R.E., Fuzzy and Neural Approaches in Engineering, Wiley, New York, 1997.
Hines, J.W., Matlab Supplement to Fuzzy and Neural Approaches in Engineering, Wiley, New York, 1997.

Computer Requirements:

ProEd Minimum Requirements: