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An Evolutionary FIR Filter Design Method
Published in Springer Berlin Heidelberg
Volume: 213
Pages: 185 - 200
In this introductory chapter, we present an evolutionary-based technique for designing one-dimensional and two-dimensional Finite Impulse Response (FIR) filters. Typically, the required filter has a given set of specifications to be met. The specifications may include cut-off frequency, band-stop region, band-pass region and ripple factors. The evolutionary method we are using is a modified version of the Genetic Algorithm (GA), which we call Flexible Genetic Algorithm (FGA). It is an optimization algorithm with high capabilities to span the space of filter parameters. FIR filters are highly required in different applications that process signals or images. A review of the state-of-the-art of filter optimization using evolutionary techniques is presented in this chapter. The aim of this work is to simply give a basic example of how filters can be designed using evolutionary techniques. As a matter of fact, medical applications require high linearity in the filter phase function to prevent undesired distortions in the detected signals. The proposed technique is based on minimizing a cost function that uses the weighted squared difference between the optimum filter specifications and the solutions generated by the evolutionary method. Comparisons between FGA-designed filter and standard method designed filters are implemented. Testing of filters is done using different noisy artificial ECG signals and selected images. We used Hermite functions to build the artificial ECG signals. {\textcopyright} 2009 Springer-Verlag Berlin Heidelberg.
About the journal
JournalData powered by TypesetEvolutionary Image Analysis and Signal Processing
PublisherData powered by TypesetSpringer Berlin Heidelberg
Open AccessNo