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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Elramsisi, A. M. Zohdy, M. A. Fadali, M. S. |
| Copyright Year | 1990 |
| Description | Author affiliation: Deprt. of Elec. and Sys Eng., Oakland University, Rochester, MI 48309-4401 (Elramsisi, A. M.; Zohdy, M. A.) || Deprt. of Elec. Eng. and Comp. Sc., University of Nevada, Reno, NV 89557. (Fadali, M. S.) |
| Abstract | Identification of the parameters and the structure of nonlinear discrete-time system models, in the joint frequency-position space, is investigated by using neural networks. A dynamical neuron model is first introduced. The neuron characteristic function NCF of the model, at higher values of signal to noise ratio SNR, resembles Gabor basis fuctions GBF. A simplification to the GBF's is also presented, where the spatial Gaussian envelope of GBF's is replaced with a triangular one. The neuron model is then encompassed in a three-layered neural net for parameter and structure identification. Simulation results, necessary modifications to improve convergence of the network, and possible extensions for this work are provided. |
| Starting Page | 3007 |
| Ending Page | 3012 |
| File Size | 334852 |
| Page Count | 6 |
| File Format | |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1990-05-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Control systems Piecewise linear techniques Neural networks Neurons Signal to noise ratio Frequency domain analysis Delay Uncertainty Convergence Steady-state NN: Neural network GBF: Gabor basis functions MGBF: Modified Gabor basis functions TGBF: Triangular gabor basis functions SNR: Signal to noise ratio equation |
| Content Type | Text |
| Resource Type | Article |
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