Least Squares Design of 2-D FIR Notch Filters Based on the Hopfield Neural Networks

被引：1

作者：

Xu, Wei ^{[1
,2
]}

Zhang, Ruihua ^{[1
,2
]}

Li, Anyu ^{[1
,2
]}

Shi, Boya ^{[1
,2
]}

Yan, Shuxia ^{[1
,2
]}

机构：

[1] Tianjin Polytech Univ, Sch Elect & Informat Engn, Tianjin 300387, Peoples R China

[2] Tianjin Key Lab Optoelect Detect Technol & Syst, Tianjin 300387, Peoples R China

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON ELECTRONICS AND ELECTRICAL ENGINEERING TECHNOLOGY (EEET 2018) | 2018年

关键词：

Hopfield neural network; Two-dimensional; FIR notch filter; Least squares; Lyapunov energy function;

D O I：

10.1145/3277453.3277474

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a design paradigm for the 2-D (two-dimensional) FIR (finite impulse response) notch filter using Hopfield neural network. A Hopfield neural network is chosen and the relationship between the least squares error criterion and the Lyapunov energy function is established. The design problem is transformed into the problem of finding the minimum value of the Lyapunov energy function. When the minimum value of the Lyapunov energy function is obtained, the outputs of the Hopfield neural network are the coefficients of the 2-D FIR notch filter. The complexity of computation can be reduced by using the Hopfield neural network. The simulation results demonstrate the effectiveness of the proposed algorithm.

引用

页码：102 / 106

页数：5

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