The Application of EMD and Genetic Neural Network Algorithm to the Dynamic Weighing System for Loader

Abstract:

The output signal of pressure sensor installed in the dynamic weighing system for loader contains strong vibration, noise, nonlinear signal. The accuracy of the dynamic weighing system is closely related to the pressure signal. An empirical mode decomposition (EMD) algorithm is proposed to preprocessing the signal contaminated. The real weighing signal is filtered out. a new method based on neural network is used to predicate the nonlinear output. in order to solve the problem that it was easily to sink into the partial minimum , the genetic algorithm was put forward .The emulation analysis and the results show that by using the above method, measure precision within 1% can be obtained.

• Periodical: Applied Mechanics and Materials (Volumes 135-136)
• Pages: 1002-1006
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.135-136.1002
• Online since: October 2011
• Authors: Qin Xian Liu, Wei Lv, Wei Bing Bao
• Keywords: BP Neural Network (BPNN), Dynamic Weighing System, Genetic Algorithm (GA), Loader

References

[1] Feina Cai, Lihua Liang, Qinxian Liu. Research and Manufacturing of Intelligent Weighing Device for Loader[J]. Construction Machinery, 2003, vol. 34(9): pp.20-22.

[2] Qinxian Liu, Yunshui Zhou, Feina Cai. Development of Loader Weighing Device Provided with GPRS Data Transmission[J]. Construction Machinery. 2007, 38(2): pp.4-6.

[3] Huang N E，Shen Z，Long S R，et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and non –stationary time series analysis[J]. Proceedings of the Royal Society Lond. 1998，A（454）: pp.903-995.

DOI: 10.1098/rspa.1998.0193

[4] Q Xie, J. P Li. The EMD Method and Its Application to Signal Processing for Infrared Gas Detection[J]. Journal of Electronics＆Information Technology. 2008, vol. 30(10): pp.2516-2519.

DOI: 10.3724/sp.j.1146.2007.00552

[5] Y. B Hou, J. Y Du, Mei Wang. Neural Network[M] . Xi'an: Publishing House of Xi'an University of Electronic Science and Technology, 2007: pp.21-25.

[6] Q. J Wang,D. CH Liao, Y. H Zhou, X. H Liao. Determination of The Topology of The Neural networks in The Prediction of Lod[J], Annals of Shanghai Astronomical Observatory, CAS, 2007(28): pp.23-29.

[7] M. Q Li, B. Y Xv, J. S Kou. On the Combination of Genetic Algorithms and Neural Networks[J]. Systems Engineering-Theory ＆ Practice. Feb. 1999, No. 2: pp.65-69.

[8] X. P Wang, L. M Cao. Genetic Algorithms- Theory Applications ＆Software Realization[M]. Xi'an: Publishing House of Xi'an Jiao Tong University. Jan. (2002).

[9] Y. J Fang, Z. W Su, S. H Li. A Method of Improving Performance of Fuzzy Neural Network Controlled Based on Hybrid Genetic Algorithm[J]. Engineering Journal of Wuhan University. 2004, 37(2): pp.74-76.