Design of Digital Filters Using Genetic Algorithms †
نویسنده
چکیده
In recent years, genetic algorithms began to be used in many disciplines such as pattern recognition, robotics, biology, and medicine to name just a few. These optimization algorithms are based on Darwins principle of natural selection which happens to be a slow process and, as a result, these algorithms tend to require a large amount of computation. However, they offer certain advantages as well over classical gradient-based optimization algorithms such as steepest-descent and Newton-type algorithms. For example, having located local suboptimal solutions they can discard them in favor of more promising local solutions and, therefore, they are more likely to obtain optimal global solutions in multimodal problems. By contrast, classical optimization algorithms though very efficient, they are not equipped to discard inferior local solutions in favour of more optimal ones. This article will explore the use of genetic algorithms for the design of several types of digital filters as follows: 1) Design of fractional-delay FIR filters 2) Design of digital IIR equalizers 3) Design of multiplierless FIR filters 4) Design of asymmetric FIR filters 5) Design of IIR filters along with a least-squares method The paper will present design methodologies, typical design examples, and experimental results obtained recently, as well as comparisons with similar designs obtained with classical methods.
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