Name
Title | ||
|---|---|---|
A Novel Principal Component Analysis Flow Pattern Identification Algorithm for Electrical Capacitance Tomography System |
Abstract | ||
|---|---|---|
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 pre-processing 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/MVHI.2010.141 | Machine Vision and Human-Machine Interface |
Keywords | Field | DocType |
principal component analysis,identification algorithm,flow pattern identification,neural network,component analysis flow pattern,electrical capacitance tomography,flow pattern identification algorithm,new algorithm,novel principal,novel principal component analysis,symmetric subspace algorithm,principal component analysis neural,electrical capacitance tomography system,bp neural network recognition,machine vision,feature selection,pattern formation,data mining,computational complexity,feature extraction,neural networks,backpropagation,neural nets,fluid flow,convergence,pattern recognition,computer networks | Feature selection,Computer science,Artificial intelligence,Artificial neural network,Electrical capacitance tomography,Computer vision,Pattern recognition,Subspace topology,Algorithm,Feature extraction,Backpropagation,Machine learning,Principal component analysis,Computational complexity theory | Conference |
ISBN | Citations | PageRank |
978-1-4244-6596-5 | 1 | 0.39 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen, Yu | 1 | 1 | 0.39 |
| Yuchen Song | 2 | 4 | 3.33 |
| Jian Zhang | 3 | 538 | 66.20 |