Study on ANFIS Application in Coal Mining Stray Current Security Prediction

Abstract:

On basis of analyzing the principles and structure of adaptive neural fuzzy inference system (ANFIS), this thesis used subtractive clustering algorithm to get fuzzy inference rule numbers and confirm the network structure. In addition, the thesis built ANFIS model adapted to coal mining workface stray current security prediction. The model can do workface stray current security prediction by the easy measured parameters of non-production field. If the stray current exceeds standard, the system will alarm on time. Moreover, the thesis compared accuracy rate of the security prediction results under different membership functions. The results indicate that the prediction accuracy of ANFIS based on subtractive clustering is the highest and its computing speed is faster. The prediction results to practical project data indicate that stray current security prediction based on ANFIS has favorable practicality and effect.

• Periodical: Key Engineering Materials (Volumes 426-427)
• Pages: 216-219
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.426-427.216
• Online since: January 2010
• Authors: C.Y. Ma, D.L. Zhang, Zhi Wang, G.X. Li, J.J. Tang
• Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS), Security Prediction, Stray Current

References

[1] Wang L and Mendel: IEEE Trans. on Neural Network, 3(5) , pp.807-814 (1992).

[2] A. Rizzi, F. M. Frattale Mascioli and G. Martinelli: Automatic Training of ANFIS Networks[G]/ IEEE International Fuzzy Systems Conference Proceedings. Seoul Korea (1999).

DOI: 10.1109/fuzzy.1999.790153

[3] Mohd Fauzi bin Othman , Thomas Moh Shan Yau. Neuro: Fuzzy Classification and Detection Technique for Bioinformatics Problems[G]. /Proceedings of the First Asia International Conference on Modelling and Simulation (AMS'07).

DOI: 10.1109/ams.2007.70

[4] Ma Cao-yuan: Coal Mining Underground Stray Current and Its Protection (Master degree thesis China University of mining and technology 2006).

[5] Wu Xiao-li: MATLAB Assisted Fuzzy System Designing (Xidian library Press, China 2008).