Authors
Li-Chun [email protected]
Computer and Intelligent Robot Program for Bachelor Degree, National Pingtung University
Chia-Nan [email protected]
Department of Automation Engineering, Nan Kai University of Technology
Available Online 30 September 2018.
DOI
https://doi.org/10.2991/jrnal.2018.5.2.15How to use a DOI?
Keywords
Quantum-behaved particle swarm optimization; Niche particle; Support vector regression; Image inspection
Abstract
This paper combines the niche particle concept and quantum-behaved particle swarm optimization (QPSO) method with chaotic mutation to train neural networks for image inspection. When exploring the methodology of reinforced quantum-behaved particle swarm (RQPSO) to train neural networks (RQPSONNs) for image inspection, first, image clustering is adopted to capture feasible information. In this research, the use of support vector regression (SVR) method determines the initial architecture of the neural networks. After initialization, the neural network architecture can be optimized by RQPSO. Then the optimal neural networks can perform image inspection. In this paper, the program of RQPSONNs for image inspection will be built. The values of root mean square error (RMSE) and peak signal to noise ratio (PSNR) are calculated to evaluate the efficiency of the RQPSONNs. Moreover, the experiment results will verify the usability of the proposed RQPSONNs for inspecting image. This research can be used in industrial automation to improve product quality and production efficiency.

Copyright
Copyright © 2018, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).