A novel principal component analysis flow pattern identification algorithm for electrical capacitance tomography system

作者：Yu Chen^*; Yuchen Song; Jian Zhang

来源：2010 International Conference on Machine Vision and Human-Machine Interface, MVHI 2010, 2010-04-24 To 2010-04-25.

DOI：10.1109/MVHI.2010.141

• Basic principles
• BP neural networks
• Convergence conditions
• Electrical Capacitance Tomography
• Electrical capacitance tomography systems
• Feature selection
• Flow pattern identification
• On flow
• Pre-processing step
• Principal Components
• Recognition rates
• Sub-space algorithms
• Subspace models
• Symmetric subspace

To solve the flow pattern identification more difficult problem in electrical capacitance tomography (ECT)technology, a novel principal component analysis flow pattern identification algorithm for neural network is presented. Based on the introduction of the basic principles of feature selection and feature extraction for principal component analysis, Construction of Symmetric subspace model based on principal component analysis neural network, and the convergence of Symmetric subspace algorithm is analyzed.The feasibility of using this algorithm for ECT is also discussed. Algorithm to meet the convergence conditions and to simplify the complex preprocessing steps, greatly reducing the computational complexity, improve the speed of the identification. Experimental results indicate that the algorithm can obtain a higher recognition rate compared with BP neural network recognition algorithm and this new algorithm presents a feasible and effective way to research on flow pattern identification algorithm of electrical capacitance tomography.

• 出版日期2010
• 单位东北林业大学