Estimation of voltage regulator stable region using radial basis function neural network

Research output: Contribution to journalArticle

Abstract

Disturbance to the voltage regulator (VR) output caused by the abrupt change in load current can be compensated using an output capacitor with an internal parasitic element called the equivalent series resistance (ESR). However, the ESR value changes due to aging and temperature change factors, thereby creating a VR stable region in terms of ESR. In practice, time-consuming and high-expertise manual characterization is required to characterize the VR stable region during the design and manufacturing phases. Therefore, this research aims to develop an efficient and effective VR characterization method. In this work, the radial basis function neural network (RBFNN) approach was implemented to estimate the stable region. Results show that the RBFNN approach yields a stable region with higher estimation accuracy and faster characterization time than those of manual characterization. VR characterization using the RBFNN approach can efficiently and effectively estimate the VR stable region.

Original languageEnglish
Pages (from-to)63-66
Number of pages4
JournalJournal of Telecommunication, Electronic and Computer Engineering
Volume10
Issue number2-8
Publication statusPublished - 1 Jan 2018

Fingerprint

Voltage regulators
Neural networks
Capacitors
Aging of materials

Keywords

  • Equivalent Series Resistance
  • Output Capacitor
  • Radial Basis Function
  • Voltage Regulator Stable Region Characterization

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

@article{95640ff868bb48908979ab006a8f095c,
title = "Estimation of voltage regulator stable region using radial basis function neural network",
abstract = "Disturbance to the voltage regulator (VR) output caused by the abrupt change in load current can be compensated using an output capacitor with an internal parasitic element called the equivalent series resistance (ESR). However, the ESR value changes due to aging and temperature change factors, thereby creating a VR stable region in terms of ESR. In practice, time-consuming and high-expertise manual characterization is required to characterize the VR stable region during the design and manufacturing phases. Therefore, this research aims to develop an efficient and effective VR characterization method. In this work, the radial basis function neural network (RBFNN) approach was implemented to estimate the stable region. Results show that the RBFNN approach yields a stable region with higher estimation accuracy and faster characterization time than those of manual characterization. VR characterization using the RBFNN approach can efficiently and effectively estimate the VR stable region.",
keywords = "Equivalent Series Resistance, Output Capacitor, Radial Basis Function, Voltage Regulator Stable Region Characterization",
author = "{Mohd Zaman}, {Mohd Hairi} and Mustafa, {Mohd. Marzuki} and Aini Hussain",
year = "2018",
month = "1",
day = "1",
language = "English",
volume = "10",
pages = "63--66",
journal = "Journal of Telecommunication, Electronic and Computer Engineering",
issn = "2180-1843",
publisher = "Universiti Teknikal Malaysia Melaka",
number = "2-8",

}

TY - JOUR

T1 - Estimation of voltage regulator stable region using radial basis function neural network

AU - Mohd Zaman, Mohd Hairi

AU - Mustafa, Mohd. Marzuki

AU - Hussain, Aini

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Disturbance to the voltage regulator (VR) output caused by the abrupt change in load current can be compensated using an output capacitor with an internal parasitic element called the equivalent series resistance (ESR). However, the ESR value changes due to aging and temperature change factors, thereby creating a VR stable region in terms of ESR. In practice, time-consuming and high-expertise manual characterization is required to characterize the VR stable region during the design and manufacturing phases. Therefore, this research aims to develop an efficient and effective VR characterization method. In this work, the radial basis function neural network (RBFNN) approach was implemented to estimate the stable region. Results show that the RBFNN approach yields a stable region with higher estimation accuracy and faster characterization time than those of manual characterization. VR characterization using the RBFNN approach can efficiently and effectively estimate the VR stable region.

AB - Disturbance to the voltage regulator (VR) output caused by the abrupt change in load current can be compensated using an output capacitor with an internal parasitic element called the equivalent series resistance (ESR). However, the ESR value changes due to aging and temperature change factors, thereby creating a VR stable region in terms of ESR. In practice, time-consuming and high-expertise manual characterization is required to characterize the VR stable region during the design and manufacturing phases. Therefore, this research aims to develop an efficient and effective VR characterization method. In this work, the radial basis function neural network (RBFNN) approach was implemented to estimate the stable region. Results show that the RBFNN approach yields a stable region with higher estimation accuracy and faster characterization time than those of manual characterization. VR characterization using the RBFNN approach can efficiently and effectively estimate the VR stable region.

KW - Equivalent Series Resistance

KW - Output Capacitor

KW - Radial Basis Function

KW - Voltage Regulator Stable Region Characterization

UR - http://www.scopus.com/inward/record.url?scp=85052027317&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052027317&partnerID=8YFLogxK

M3 - Article

VL - 10

SP - 63

EP - 66

JO - Journal of Telecommunication, Electronic and Computer Engineering

JF - Journal of Telecommunication, Electronic and Computer Engineering

SN - 2180-1843

IS - 2-8

ER -