Week 
Lectures 
Laboratories 
Texts 
Mfiles 
Projects 
1 
Basic MATLAB commands, matrix and vector opperations, data structures, graphics, file management, SIMULINK 


2

Matrix description of neural networks, transfer functions, classification of neural networks, selected applications

Basic computational algorithms, error surface, selected commands: HARDLIM, TANSIG, LOGSIG, PURELIN, ERRSURF



3

Fundamental model of a neuron (PERCEPTRON), network training, princple of classification

Perceptron learning process, selected commands: RANDS, INITP, LEARNP, TRAINP, PLOTPC, PLOTPV



4

Linear neural network (ADALINE), model of a neuron and network architecture, the learning proces

Adaptive linear network, selected commands: INITLIN, LEARNWH, TRAINWH, SOLVELIN, SIM, ADAPTWH



5


Project NN1: Adaptive rejection of signal components




6

Multilayer neural network, model of a neuron and network architecture, LevenbergMarquardtova method, timeseries prediction

Basic algorithms of the learning process, optimization of network coefficients, selected commands: INITFF, SIM, LEARNBP, TRANBP



7


Project NN2: Analysis and prediction of realtime series




8


CASE STUDY: Adaptive Signal Processing and Adaptive Neural Networks (B. Widraw)



9

Functions of radial type, network model, selection of network structure

Network optimization, selected commands: RADBAS, SOLVERB



10

Associative learning methods, Hebb method, Kohonen method

Network traing, selected commands: LEARNH, LEARNK



11

Selforganizing networks, inicialization and training, selforganizing maps

Selforganizing maps, selected commands: NBDIST, NBGRID, COMPET



12


Project NN3: Classification of EEG signal segments




13

Modelling of neural networks in the SIMULINK environment

Basic SIMULINK blocks for neural networks modelling, noise reduction



14


Conclusion


