Abstract
A quasi-equiripple one- and two-dimensional linear-phase FIR digital filters design approach is proposed based on a novel neural network optimization technique. Its goal is to minimize the weighted square-error function in the frequency domain. The design solution is presented as a parallel algorithm to approximate the desired frequency response specification, and the weight coefficients are updated according to the error function. Thus, the proposed approximation method can avoid the overshoot phenomenon which may happen near the pass-band and stop-band edges of the designed filter, and may make a fast calculation of the filter’s coefficients possible. Several optimal design examples are given to illustrate the effectiveness of the proposed approach.
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© 2007 Springer Berlin Heidelberg
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Wang, X., He, Y., Peng, Y. (2007). Neural Network Approach for Designing One- and Two-Dimensional Quasi-Equiripple FIR Digital Filters. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_106
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DOI: https://doi.org/10.1007/978-3-540-72395-0_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
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