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

作     者：Lai, Li-Chun Ko, Chia-Nan 

作者机构：Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Neipu Taiwan Nan Kai Univ Technol Dept Automat Engn Caotun Nantou Taiwan 

出 版 物：《JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE》 

年 卷 期：2018年第5卷第2期

页      面：139-143页

基　　金：Ministry of Science and Technology  R.O.C. [MOST 106-2221-E-252-001] 

主　　题：Quantum-behaved particle swarm optimization Niche particle Support vector regression Image inspection 

摘      要：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.