Title:  Neural Networks 

Code:  NEU 

Ac.Year:  ukončen 2005/2006 

Term:  Winter 

Curriculums:  

Language:  Czech 

Credits:  6 

Completion:  examination (written) 

Type of instruction:  Hour/sem  Lectures  Sem. Exercises  Lab. exercises  Comp. exercises  Other 

Hours:  39  0  0  0  26 

 Examination  Tests  Exercises  Laboratories  Other 

Points:  55  20  0  0  25 



Guarantee:  Zbořil František V., doc. Ing., CSc., DITS 

Lecturer:  Zbořil František V., doc. Ing., CSc., DITS 
Instructor:  Drahanský Martin, prof. Ing., Dipl.Ing., Ph.D., DITS Orság Filip, Ing., Ph.D., DITS 

Faculty:  Faculty of Information Technology BUT 

Department:  Department of Intelligent Systems FIT BUT 

Prerequisites:  

 Learning objectives: 

  To give the students the knowledge of fundamentals of neural network theory and the knowledge of topologies, learning, responses and possible practical applications of various types of these networks.  Description: 

  Artificial neuron, basis and activation functions. Classification of neural networks. Principles of individual neural networks (topology, learning, responses, typical applications): "Adaline, Perceptron, Madaline, BPN, adaptive feedforward multilayer networks, selforganizing neural networks, CPN, LVQ, RBF, RCE, Hopfield neural networks, BAM, SDM, Boltzmann machine, Neocognitron". Genetic algorithm, fuzzy systems, rough sets and neural networks.  Knowledge and skills required for the course: 

  Fundamentals of mathematical analysis and probability calculus.  Learning outcomes and competences: 

  Students acquire knowledge of particular types of neural networks and so they will be able to design programs using these networks to solving of various practical problems.  Syllabus of lectures: 


 Introduction, artificial neuron, classification of neural networks
 Perceptron, Adaline, Madaline
 Bacpropagation (BP) Neural Network
 Constructive neural networks
 RBF and RCE neural networks
 Topologic organized neural network, CPN, LVQ
 ART and SDM neural networks
 Neural networks as associative memories, Hopfield network, BAM
 Optimization problems solving using neural networks, Stochastic neural networks, Boltzmann machine
 Neocognitron neural network
 Genetic algorithm and neural networks
 Fuzzy systems and neural networks
 Rough sets and neural networks
 Syllabus  others, projects and individual work of students: 


 Individual projects  Using of particular neural network for solving some practical problems (as classification, optimization, association)
 Fundamental literature: 


 Mehrotra,K., Mohan,C.K., Ranka S: Artificial Neural Networks, The MIT Press, 1997, ISBN 0262133288
 Hassoun, M.H.: Artificial Neural Networks, The MIT Press, 1995, ISBN 026208239X
 Haykin,S.: Neural Networks, Macmillan College Publishing Company, Inc., 1994, ISBN 0023527617
 Study literature: 


 Sima,J., Neruda,R.: Theoretical questions of neural networks, MATFYZPRESS, 1996, ISBN 8085863189
 Novak,M. and others: Artificial neural networks, C.H. Beck, 1998, ISBN 8071791326
 Progress assessment: 

 
 Midterm written examination  20 points
 Project  25 points
 
