Grey neural network-based forecasting system for vision-guided robot trajectory tracking

Shih-Hung Yang, Chung Hsien Chou, Chen Fang Chung, Wen Pang Pai, Tse Han Liu, Yung Sheng Chang, Jung Che Li, Huan Chan Ting, Yon Ping Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents a grey neural network-based forecasting system (GNNFS) in solving the prediction problem. GNNFS adopts a grey model to predict the signal and a neural network (NN) to forecast the prediction error of the grey model. A sequential batch learning (SBL) is developed to adjust the weights of the NN. The proposed GNNFS is applied to a binocular robot, called an Eye-Robot, for human-robot interaction which involved predicting the trajectory of a participant's hand and tracking the hand. By applying the SBL, the GNNFS can gradually learn to predict the trajectory of the hand and track it well. The experimental results show that the GNNFS can carry out the SBL in real-time for vision-guided robot trajectory tracking.

Original languageEnglish
Title of host publicationICCAS 2011 - 2011 11th International Conference on Control, Automation and Systems
Pages1512-1517
Number of pages6
Publication statusPublished - 2011 Dec 1
Event2011 11th International Conference on Control, Automation and Systems, ICCAS 2011 - Gyeonggi-do, Korea, Republic of
Duration: 2011 Oct 262011 Oct 29

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833

Conference

Conference2011 11th International Conference on Control, Automation and Systems, ICCAS 2011
CountryKorea, Republic of
CityGyeonggi-do
Period11-10-2611-10-29

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Grey neural network-based forecasting system for vision-guided robot trajectory tracking'. Together they form a unique fingerprint.

Cite this