Fuzzy systems and neural networks have recently become widely applied to various areas including consumer products, mechatronics systems, industrial process control, information systems, pattern and speech recognition, and prediction of future stock prices to name a few. Fuzzy logic and neural networks share the common ability to improve the decision making process for systems characterized by ill-defined dynamics and working in an imprecise environment. For fuzzy systems this is done through linguistic description of the system by combining fuzzy sets with fuzzy rules following a well-structured numerical estimation procedure. For neural networks, this is done through detecting patterns and relationships from a set of training input-output data gathered from the system, while learning from relationships and adapting to change. The course is mainly intended as introductory material for fuzzy logic and neural networks and outlines the most recent developments in these areas and their applications for intelligent systems design. [Offered: W] Prereq: (Level at least 3B Systems Design Engineering) or (Level at least 4A Mechatronics Engineering) or Mechatronics Option
