Implementation of hybrid ANN-PSO algorithm on FPGA for harmonic estimation

被引：63
作者：
Vasumathi, B. [1 ]
Moorthi, S. [1 ]
机构：
[1] Natl Inst Technol, Dept Elect & Elect Engn, Tiruchirappalli 15, Tamil Nadu, India
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2012年 / 25卷 / 03期
关键词：
Adaline; Adaptive Neural Network (ANN); Particle Swarm Optimization (PSO); Adaptive Neural Network-Particle Swarm Optimization (ANN-PSO); Field Programmable Gate Arrays (FPGA); Harmonics; PARTICLE SWARM OPTIMIZATION; DESIGN; IDENTIFICATION; CONTROLLER; MODEL;
D O I：
10.1016/j.engappai.2011.12.005
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Harmonic estimation is the main process in active filters for harmonic reduction. A hybrid Adaptive Neural Network-Particle Swarm Optimization (ANN-PSO) algorithm is being proposed for harmonic isolation. Originally Fourier Transformation is used to analyze a distorted wave. In order to improve the convergence rate and processing speed an Adaptive Neural Network Algorithm called Adaline has then been used. A further improvement has been provided to reduce the error and increase the fineness of harmonic isolation by combining PSO algorithm with Adaline algorithm. The inertia weight factor of PSO is combined along with the weight factor of Adaline and trained in Neural Network environment for better results. ANN-PSO provides uniform convergence with the convergence rate comparable that of Adaline algorithm. The proposed ANN-PSO algorithm is implemented on an FPGA. To validate the performance of ANN-PSO; results are compared with Adaline algorithm and presented herein. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：476 / 483
页数：8
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