Open AccessDesign of sparse cosine modulated filter banks based on Hopfield neural network
Author(s) -
Wei Xu,
Xin Gao,
Jiaxiang Zhao
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/3/032034
Subject(s) - matching pursuit , computer science , filter (signal processing) , artificial neural network , set (abstract data type) , linear phase , algorithm , filter bank , discrete cosine transform , filter design , finite impulse response , signal (programming language) , trigonometric functions , control theory (sociology) , artificial intelligence , mathematics , compressed sensing , computer vision , image (mathematics) , geometry , control (management) , programming language
Cosine modulated filter bank (CMFB) is the most basic and important module in multi-rate signal processing system. Sparse FIR linear-phase CMFB can not only reduce the complexity of hardware design, but also ensure good performance. In this paper, a new design method of CMFB is proposed. First, the set of sparse coefficients of the prototype filter is found by using the orthogonal matching pursuit (OMP) algorithm. Then the Hopfield neural network (HNN) is employed to optimize the non-zero coefficients. The analytical and simulation results demonstrate that the new approach not only ensures that the sparse CMFBs satisfy near perfect reconstruction (NPR), but also improves the efficiency of hardware operation.