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Least-squares design of digital differentiators using neural networks with closed-form derivations

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1 Author(s)
Yue-Dar Jou ; Dept. of Comput. & Inf. Sci., Mil. Acad., Kaohsiung, Taiwan

In this letter, a neural network implementation for the least-squares design of digital differentiators with closed-form derivations for Hopfield-related parameters are proposed. Using this technique, the optimal filter coefficients are obtained by iteratively updating the dynamic nonlinear equations of the network. Simulation results indicate that the proposed technique has the advantage of effectiveness and is suitable for hardware implementation in real time.

Published in:

Signal Processing Letters, IEEE  (Volume:12 ,  Issue: 11 )

Date of Publication:

Nov. 2005

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