Title :
Nonlinear signal processing with self-organizing neural networks
Author :
Gao, Keqin ; Ahmad, M. Omair ; Swamy, M.N.S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada
Abstract :
The application of self-organizing neural networks in processing nonlinear dynamic signals directly is investigated. The processing of a signal uses a model-based approach. The signal generating system is modeled by decomposing it into simpler subsystems and each subsystems is associated with a neuron on a single-layer network. Each subsystem is implemented using a temporally local linear combiner. The network is trained with a self-organizing procedure and the parameters of the linear combiners are updated by using the Widrow-Hoff adaptive rule. A competitive rule which takes into consideration the temporal dependence among the signal samples is presented. Simulation results are presented to illustrate the method
Keywords :
neural nets; signal processing; Widrow-Hoff adaptive rule; model-based approach; nonlinear dynamic signals; self-organizing neural networks; signal processing; single-layer network; subsystems; temporal dependence; temporally local linear combiner; Adaptive signal processing; Linear systems; Neural networks; Neurons; Nonlinear systems; Organizing; Predictive models; Signal generators; Signal processing; System identification;
Conference_Titel :
Circuits and Systems, 1991., IEEE International Sympoisum on
Print_ISBN :
0-7803-0050-5
DOI :
10.1109/ISCAS.1991.176635