Prediction systems based on FIR BP neural networks

Paper deals with experience of application of FIR filter (Finite Impulse Response) in BP neural networks used for prediction system. Prediction can be defined as extrapolation of unknown function determine by representing data sets. One of the most difficult problems in prediction systems is determination of the size of the time series variable input. It is always very difficult to determine the right size of the "history" of input variable necessary for prediction - extrapolation of the modeled function. So in general prediction problem can have input e.g. ( x(t), x(t-1),...,x(t-tau)), where tau - determine the width of the input window size and output (y(t+1), y(t+2),..., y(t+lambda)), where lambda - is size of the length of the time series of the predicted variable and x and y are vectors. Usually both tau and lambda depend on the problem and in case of output the size of the lambda is small then there is bigger chance that prediction will be more reliable. The avoid a problem of tau determination a FIR neural networks offer solution to gather "history" of the unknown function inside of neural network topology, namely on neural networks synapses in the form of FIR filters.