OS8-3 Reinforced Quantum-behaved Particle Swarm Optimization Based Neural Networks for Image Inspection

Title:	OS8-3 Reinforced Quantum-behaved Particle Swarm Optimization Based Neural Networks for Image Inspection
Publication:	ICAROB2018
Volume:	23
Pages:	423-426
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2018.OS8-3
Author(s):	Li-Chun Lai, Chia-Nan Ko
Publication Date:	February 2, 2018
Keywords:	Quantum-behaved particle swarm optimization, Niche particle, Support vector regression, Image inspection
Abstract:	The 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 constructing the reinforced quantum-behaved particle swarm (RQPSO) to train neural networks (RQPSONNs) for image inspection, first, image clustering is adopted to capture feasible information. Then the database of image can be built. 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.
PDF File:	https://alife-robotics.co.jp/members2018/icarob/data/html/data/OS_pdf/OS8/OS8-3.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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