Study on ANN Noise Adaptability in Application of Industry Process Characteristics Mining

Abstract:

One important technical solution to improve the efficiency and environment-friendly of a running industry process is to grasp its characteristics by data miming tools such as ANN. As field data are usually polluted by noise more or less and, even badly polluted sometimes, the noise adaptability of ANNs such as BP, RBF and fuzzy neural networks built on BP or RBF is analyzed in a quite thorough way. Firstly, as for computer simulation study, a MIMO non-linear process is supposed to be polluted by 3 different levels of white noise so as to observe and analyze the mining performances of the above 4 kinds ANNs. Then, their performances in characteristics mining of the combustion process of a running 600MWe boiler are further analyzed as a practical application case, which is polluted even more seriously by field data noise. The results show that noise adaptability cannot be ignored in the selection of data mining tools in engineering application. The ascending strong sort order of noise adaptability is BP, RBF and the fuzzy ones built on BP and RBF respectively. Fuzzy neural networks built on RBF are recommended for those complicated and noisy application like the boiler combustion case.

• Periodical: Advanced Materials Research (Volume 462)
• Pages: 635-640
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.462.635
• Online since: February 2012
• Authors: Xian Huang
• Keywords: Fuzzy Neural Network (FNN), Industry Process, Neural Network (NN), Noise

References

[1] Tian Jingwen. Study and application of artificial neural networks algorithms. Beijing Institute of Technology Press. Beijing (2006).

[2] Cai Kunbao, Wang Chengliang, Chen Zenghan. A method for generating standard Gaussian white noise sequences. Proceedings of the Chinese Society for Electrical Engineering, 2004, 24(12): 207~211.

[3] Huang Xian, Wang Hui. Modeling of combustion process characteristics of a 600 MWe boiler by T-S fuzzy neural networks. International Conference on Energy and Environment Technology, Guilin China, 2009: 737~740.

DOI: 10.1109/iceet.2009.185

[4] Kanungo T, Mount D M, Netanyahu N S. An efficient K-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 881~892.

DOI: 10.1109/tpami.2002.1017616

[5] Yuan Zengren. Artificial neural networks and their applications. Tsinghua University Press. Beijing (1999).