Polynomial-Perceptron Based Adaptive Equalizer with a Robust Liarning Algorithm
博士 === 國立交通大學 === 電子研究所 === 84 === in the following two respects. First, a single-layer non- linear decision feedback equalizer (DFE) equipped with polynomial-perceptron model of nonlinearities is developed. Second, an lp-norm based learning algorithm sui...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
1995
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| Online Access: | http://ndltd.ncl.edu.tw/handle/89558594992130601060 |
| Summary: | 博士 === 國立交通大學 === 電子研究所 === 84 === in the following two respects. First, a single-layer non-
linear decision feedback equalizer (DFE) equipped with
polynomial-perceptron model of nonlinearities is developed.
Second, an lp-norm based learning algorithm suitable for the
addresseed structure is investigated. The structure exerts the
benefit of using a DFE and achieves the required non-
linearities in a single-layer net. This is advantageous since
it is much easier to train by a stochastic gradient algorithm .
It is shown that the algorithm using lp-norm error cir- terion
with p<2 is robust to deal with the error for the new equalizer
and hence better convergence property is obtained. Detailed
performance analysis with a consideration on the possible
numerical problem for p<1 is performed. Computer simulations
show that the new equalizer is satisfactory in both convergence
rate and bit error rate(BER) performance. Also, our scheme is
shown capable of achieving the performanc offered by a minimum
BER equalizer. In some case, a per- formance quite close to the
maximum-likelihood sequence estimation(MLSE)criterion can also
be attained.
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