Applications of Artificial Neural Networks and Fuzzy Models—
an Engineering Approach

 

Course Outline

Monday–Wednesday, May 20–22, 2013

Day 1: Monday, May 20

Morning Session

  • Introduction to artificial neural networks (ANNs)
  • Pattern classification using back-propagation ANNs with classification examples
  • Conventional (Bayesian) pattern classification

Afternoon Session

  • Designing ANNs to achieve optimal performance through iterative training and validation
  • Comparison of performances between ANN-based and Bayesian Rule-based classifiers for a bioinformatics problem

Day 2: Tuesday, May 21

Morning Session

  • Design ANNs for screening activities in anticancer analogues with optimization steps applied on input data
  • Taxol analogues project using additional optimization steps in ANNs design

Afternoon Session

  • Introduction to fuzzy models with classification examples
  • Combination of ANN and fuzzy models into neuro-fuzzy prototypes

Day 3: Wednesday, May 22

Morning Session

  • Application of the ANNs in high throughput screening (HTS) of the chemical compound libraries
  • Extension of HTS with the example of applied fuzzy logic

Afternoon Session

  • Kohonen’s Self Organizing Map or neural network
  • Hopfield Network for classification and optimization
  • Future applications

For More Information Contact:

Kevin Curry, Program Manager
Center for Engineering & Interdisciplinary Professional Education
KU Continuing Education
1515 Saint Andrews Drive, Lawrence, KS 66047
785.864.7861
kgcurry@ku.edu


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