Title :
Growing filters for finite impulse response networks
Author :
Diepenhorst, M. ; Nijhuis, J.A.G. ; Venema, R.S. ; Spaanenburg, L.
Author_Institution :
Dept. of Comput. Sci., Groningen Univ., Netherlands
Abstract :
Time-delay neural networks are well-suited for prediction purposes. A particular implementation is the finite impulse response (FIR) neural net. A major design problem exists in establishing the optimal order of such filters while minimizing the number of weights. Here, a constructive solution inspired by cascade learning is outlined and illustrated by some typical case-studies
Keywords :
FIR filters; forecasting theory; learning (artificial intelligence); neural nets; time series; cascade learning; finite impulse response networks; prediction; time-delay neural networ; Auditory system; Biological processes; Biological system modeling; Delay; Feedforward systems; Finite impulse response filter; Information filtering; Information filters; Neural networks; Neurons;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487530
