基于FPGA实现了一种用于ARMA模型参数辨识的遗传算法

ACM Great Lakes Symposium on VLSI Pub Date : 2014-05-20 DOI:10.1145/2591513.2591579

H. Merabti, D. Massicotte

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摘要

在本文中，我们提出了一种用于线性和非线性自回归移动平均(ARMA)模型参数识别的遗传算法(GA)的FPGA实现。该遗传算法的特点是专门为自适应滤波应用设计了遗传算子。该设计使用非常低的位字长定点表示来实现，其中只使用了6位字长算法。实现实验表明，该方法具有较高的参数识别能力和较低的占用空间。

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FPGA based implementation of a genetic algorithm for ARMA model parameters identification

In this paper, we propose an FPGA implementation of a genetic algorithm (GA) for linear and nonlinear auto regressive moving average (ARMA) model parameters identification. The GA features specifically designed genetic operators for adaptive filtering applications. The design was implemented using very low bit-wordlength fixed-point representation, where only 6-bit wordlength arithmetic was used. The implementation experiments show high parameters identification capabilities and low footprint.

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ACM Great Lakes Symposium on VLSI

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