University of Wisconsin Madison
Introduction to Artificial Neural Network and Fuzzy Systems (E C E 539) Syllabus
Course Learning Outcomes
    Course Learning Outcome
  • 1
    1. Students will be able to determine if a given data analylsis task is a pattern classification problem or a model approximation problem.
  • 2
    2. Students will be able to apply multi-layer perceptron neural network training algorithm to develop artificial neural network (ANN) based pattern classifiers and data predictors.
  • 3
    3. Students will be able to apply support vector machine (SVM) to develop pattern classifiers.
  • 4
    4. Students will be able to apply radial basis function to model given data sets.
  • 5
    5. Students will be able to apply self organization map and k-means to perform clustering operations of a given data set.
  • 6
    6. Students will be able to develop a fuzzy logic controller to perform simple control task on a given data set.
  • 7
    7. Students will be able to apply stochastic optimization methods, including simulated annealing, genetic algorithm and random search to solve a discrete optimization problem.
Introduction to Artificial Neural Network and Fuzzy Systems
E C E 539 ( 3 Credits )
Theory and applications of artificial neural networks and fuzzy logic: multi-layer perceptron, self-organization map, radial basis network, Hopfield network, recurrent network, fuzzy set theory, fuzzy logic control, adaptive fuzzy neural network, genetic algorithm, and evolution computing. Applications to control, pattern recognition, nonlinear system modeling, speech and image processing.
Comp Sci 302, or Comp Sci 310, or knowledge of C programming lang
College: College of Engineering
Instructor Name
Instructor Campus Address
Contact Hours
Course Coordinator
Text book, title, author, and year
Neural Networks: A Comprehensive Foundation, Simon Haykin, Prentice Hall, New Jersey, second edition, 1999.
Supplemental Materials
Required / Elective / Selected Elective
Selected Elective
ABET Program Outcomes Associated with this Course
Program Specific Student Outcomes
Brief List of Topics to be Covered
  1. Learning paradigms, perceptron learning
  2. Multi-Layer Perceptron and Back-propagation learning
  3. Pattern classification
  4. Support vector machines
  5. Clustering, Self-Organization Map, Radial Basis Network,
  6. Time series analysis, system identification and expert system applications
  7. Fuzzy Set Theory and Fuzzy Logic Control
  8. Genetic Algorithm and Evolution Computing
  9. Recurrent Network, Hopfield network (time permit)
Additional Information
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