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Fuzzy, Evolutionary and Neuro-computing
Abbreviation: Load: 30(L) + 0(E) + 0(LE) + 0(CE)
Lecturers in charge: Prof. dr. sc. Bojana Dalbelo-Bašić
Prof. dr. sc. Marin Golub
Course description: Fuzzy, evolutionary and neuro-computation is a group of methods that differ from classical computational methods in very fundamental ideas. They are based on approximate reasoning, self learning, parallelism and non-determinism. Inspiration for these methods comes from biology e.g. biological neuron, process of evolution, human like approximate reasoning etc. These methods can solve problems that cannot be solved by applying classical mathematical and computational methods and they are used in scientific research and in myriad applications and everyday products.
Lecture languages: - - -
Compulsory literature:
1. Genetic Algorithms Data Structures = Evolution Programs;Z.Michalewicz;1992;Springer Verlag, Berlin
2. Neural Networks, Comprehensive Foundation;S.Haykin;1999;Prentice Hall
3. Fuzzy Logic;J. Yen and R. Langari;1999;Prentice Hall
Recommended literature:
4. Fuzzy Set Theory and Its Applications;H.J.Zimmermann;1991;Kluwer Academic Publishers, Second Edition
5. Practical Genetic Algorithms;R. L. Haupt, S. E. Haupt;2004;Wiley-Interscience; 2 Ed.
6. Handbook of Genetic Algorithms;L. Davis;1991;Van Nostrand Reinhold, New York
L - Lectures
E - Exercises
LE - Laboratory exercises
CE - Project laboratory
* - Not graded
Copyright (c) 2006. Ministarstva znanosti, obrazovanja i športa. Sva prava zadržana.
Programska podrška (c) 2006. Fakultet elektrotehnike i računarstva.
Oblikovanje(c) 2006. Listopad Web Studio.
Posljednja izmjena 2011-07-19