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

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.