Design of sparse cosine modulated filter banks based on Hopfield neural network

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Xu, Wei^1^2 ; Gao, Xin^1 ; Zhao, Jiaxiang^3

School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin

300387, China^1

Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin

300387, China^2

College of Electronic Information and Optical Engineering, Nankai University, Tianjin

300081, China^3

关键词: Cosine modulated filter banks;    Hardware design;    Hopfield neural networks (HNN);    Multirate signal processing;    Near perfect reconstruction;    Non-zero coefficients;    Orthogonal matching pursuit;    Prototype filters;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/3/032034/pdf
DOI  :  10.1088/1757-899X/569/3/032034

来源: IOP

【 摘 要 】

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.