Superior performance of using hyperbolic sine activation functions in ZNN illustrated via time-varying matrix square roots finding
A special class of recurrent neural network (RNN), termed Zhang neural network (ZNN) depicted in the implicit dynamics, has recently been proposed for online solution of time-varying matrix square roots. Such a ZNN model can be constructed by using monotonically-increasing odd activation functions t...
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| Published in: | Computer Science and Information Systems Vol. 9; no. 4; pp. 1603 - 1625 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
2012
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| ISSN: | 1820-0214, 2406-1018 |
| Online Access: | Get full text |
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| Summary: | A special class of recurrent neural network (RNN), termed Zhang neural
network (ZNN) depicted in the implicit dynamics, has recently been proposed
for online solution of time-varying matrix square roots. Such a ZNN model can
be constructed by using monotonically-increasing odd activation functions to
obtain the theoretical time-varying matrix square roots in an error-free
manner. Different choices of activation function arrays may lead to different
performance of the ZNN model. Generally speaking, ZNN model using hyperbolic
sine activation functions may achieve better performance, as compared with
those using other activation functions. In this paper, to pursue the superior
convergence and robustness properties, hyperbolic sine activation functions
are applied to the ZNN model for online solution of time-varying matrix
square roots. Theoretical analysis and computer-simulation results further
demonstrate the superior performance of the ZNN model using hyperbolic sine
activation functions in the context of large model-implementation errors, in
comparison with that using linear activation functions.
nema |
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| ISSN: | 1820-0214 2406-1018 |
| DOI: | 10.2298/CSIS120121043Z |