A Supervised Learning Method for the Design of Linear Phase FIR Digital Filter Using Keras

被引：5

作者：

Tseng, Chien-Cheng ^{[1
]}

Lee, Su-Ling ^{[2
]}

机构：

[1] Natl Kaohsiung Univ Sci & Tech, Dept Comp & Commun Engn, Kaohsiung, Taiwan

[2] Chang Jung Christian Univ, Dept Comp Sci & Informat Engn, Tainan, Taiwan

来源：

2019 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS) | 2019年

关键词：

digital filter; FIR filter; linear phase; supervised learning; deep learning;

D O I：

10.1109/ispacs48206.2019.8986402

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a supervised learning method for the design of linear phase FIR digital filter using Keras is presented. First, the design problem of the linear phase finite impulse response (FIR) digital filter is transformed to a supervised learning problem. Then, the optimizers in Keras framework are used to determine the filter coefficients by minimizing the mean squared error (MSE) loss function. The widely-used optimizers include adaptive moments (Adam) algorithm and stochastic gradient descent (SGD) with momentum algorithm. Finally, the numerical design examples of low-pass and high-pass FIR digital filters are demonstrated to show the usefulness of the supervised learning method with Keras framework.

引用

页数：2