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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Georgiou, G.M. Koutsougeras, C. |
| Copyright Year | 1992 |
| Abstract | The backpropagation algorithm is extended to complex domain backpropagation (CDBP) which can be used to train neural networks for which the inputs, weights, activation functions, and outputs are complex-valued. Previous derivations of CDBP were necessarily admitting activation functions that have singularities, which is highly undesirable. In the derivation, CDBP is derived so that that it accommodates classes of suitable activation functions. One such function is found and the circuit implementation of the corresponding neuron is given. CDBP hardware circuits can be used to process sinusoidal signals all at the same frequency (phasors).< |
| Sponsorship | IEEE Circuits and Systems Society |
| Starting Page | 330 |
| Ending Page | 334 |
| Page Count | 5 |
| File Size | 335380 |
| File Format | |
| ISSN | 10577130 |
| Volume Number | 39 |
| Issue Number | 5 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1992-05-01 |
| Publisher Place | U.S.A. |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Backpropagation algorithms Neurons Neural networks Circuits Feedforward systems Least squares approximation Frequency Neural network hardware Nonhomogeneous media Signal processing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering |
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