The Identification of the Flame Combustion Stability by Combining Principal Component Analysis and BP Neural Network Techniques


The identification of flame stability plays a vital role in the industrial production of plants. This paper presents a novel approach to the identification of the flame combustion stability. The various features that can reflect the characteristics of flame signal are extracted from the time and frequency domains of the signal. Based on the principal component analysis (PCA), the five features extracted are compressed into two features, which is very convenient to carry out the pattern identification following. The back-propagation neural network (BP neural network), as a good method of pattern identification, is applied to train the samplings. The results obtained demonstrate that the approach combining principal component analysis and BP neural network is very suitable for the identification of the flame combustion stability. combustion stability.

  • Abstract
  • Keywords
  • Introduction
  • Methodology
  • Experimental Setup and Process
  • Results and Discussion
  • Conclusion
  • Acknowledgments
  • References

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