Title :
System identification using a Fourier/Hopfield neural network
Author :
Karam, Marc ; Fadali, M. Sami
Author_Institution :
Dept. of Electr. Eng., Tuskegee Univ., AL, USA
Abstract :
We use a Fourier/Hopfield network for the identification of a periodic system based on input-output data. The network automatically optimizes the model parameters and adapts by adding more neurons until the modeling error drops below a specified level. We show that, for periodic systems, Fourier basis functions are the best choice for system identification
Keywords :
Fourier transforms; Hopfield neural nets; identification; time-varying systems; Fourier basis functions; Fourier/Hopfield neural network; input-output data; model parameters; modeling error; neurons; periodic system; system identification; Computer simulation; Hopfield neural networks; Least squares approximation; Mean square error methods; Neural networks; Neurons; Noise generators; Nonlinear systems; System identification; Training data;
Conference_Titel :
Circuits and Systems, 2001. MWSCAS 2001. Proceedings of the 44th IEEE 2001 Midwest Symposium on
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-7150-X
DOI :
10.1109/MWSCAS.2001.986271
