Neural weighted least-squares design of FIR higher-order digital differentiators

Authors:
Yue-Dar Jou;Fu-Kun Chen;Chao-Ming Sun
Affiliations:
Department of Electrical Engineering, ROC Military Academy, Taiwan;Department of Computer Science and Information Engineering, Southern Taiwan University;Department of Management Sciences, ROC Military Academy, Taiwan
Venue:
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Year:
2009

Citing 3
Cited 0

Design of efficient second and higher order FIR digital differentiators for low frequencies

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Genetic algorithm approach for designing higher-order digital differentiators

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Analytic closed-form matrix for designing higher order digitaldifferentiators using eigen-approach

IEEE Transactions on Signal Processing

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Abstract

This paper extends the neural network based algorithm for equiripple design of higher-order digital differentiators in the weighted least-squares sense. The proposed approach fonnulates an error representation reflecting the difference between the desired amplitude response and the designed response in a Lyapunov error function. The optimal filter coefficients are obtained when neural network achieves convergence. Furthermore, by using a weighted updating function, the proposed method can find a very good approximation of the minimax solution. Simulation results indicate that the proposed technique is able to achieve good performance in a parallelism manner.