Reinforced Quantum-behaved Particle Swarm Optimization Based Neural Networks for Image Inspection

DOI

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 Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Li-Chun Lai
AU  - Chia-Nan Ko
PY  - 2018
DA  - 2018/09/30
TI  - Reinforced Quantum-behaved Particle Swarm Optimization Based Neural Networks for Image Inspection
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 139
EP  - 143
VL  - 5
IS  - 2
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2018.5.2.15
DO  - 10.2991/jrnal.2018.5.2.15
ID  - Lai2018
ER  -