Computational Intelligence II

Subject: 

Computational Intelligence 2

Goal:

 Introduction to the Artificial Neural Networks practical applications.

Content of the subject:

  1. Lesson – Computational Intelligence, Intelligent Systems.
  2. Lesson – Motivation. Where are the ANNs usable?
  3. Lesson – Biological Motivation, Neuron, Definition of ANN.
  4. Lesson – The Theory of ANNs, Data Sets for training and testing the ANNs.
  5. Lesson – Supervised Learning, Data, Algorithms 1.
  6. Lesson – Supervised Learning, Data, Algorithms 2.
  7. Lesson – Unsupervised Learning, Data, Algorithms 1.
  8. Lesson – Unsupervised Learning, Data, Algorithms 2.
  9. Lesson – ANN usage - Cognitive Science.
  10. Lesson – ANN usage - Clustering, Classification.
  11. Lesson – ANN usage - Prediction, Process control.
  12. Lesson – Summary and repeating.
  13. Lesson – Other Applications of ANN.

Seminars:

1.      Introduction to the seminar organization and program, demonstration of the remote laboratory, giving semester projects

2.      Data, data sets, analysis of data sets used within the semester – circle in a square, spiral in a square, breast cancer, questionnaires from companies.

3.      Data sets processing by means of statistical methods.

4.      Data sets processing by means of statistical methods.

5.      Perceptron – programming. Dichotomous classification by means of the perceptron.

6.      Classification by means of ANN – supervised learning. Verification on circle in a square and spiral in a square data sets.

7.      Classification by means of ANN – unsupervised learning. Clustering. Demonstration on circle in a square and spiral in a square data sets.

8.      Prediction by means of ANN – testing on breast cancer and questionnaires from companies data sets.

9.      Regulation and control by means of ANN.

10.  Experiments with robotic model control.

11.  Experiments with robotic model control.

12.  Experiments with robotic model control.

13.  Results of semester projects presentation, credits.

Literature:

  • DE CASTRO, L. N.: Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall/CRC Computer & Information Science Series 2006.
  • FINN, A. a SCHEDING, S.: Developments and Challenges for Autonomous Unmanned Vehicles: A Compendium (Intelligent Systems Reference Library). Springer 2010.
  • FLOREANO, D. a MATTIUSSI, C.: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (Intelligent Robotics and Autonomous Agents). The MIT Press 2008.
  • GE, S. S.: Autonomous Mobile Robots: Sensing, Control, Decision Making and Applications (Automation and Control Engineering). CRC Press 2006.
  • HARVEY, R. L.: Neural Network Principles. Prentice-Hall 1994.
  • HRISTEV, R. M.: The ANN Book. GNU Public Licence, 1998.
  • RUSSELL, C. E. a YUHUI, S.: Computational Intelligence: Concepts to Implementations. Morgan Kaufmann 2007.
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