Abstract
Forecasting model of coal rock electromagnetic radiation was built with combining time series analyze and adaptive neuro-fuzzy inference system (ANFIS). In the first, coal rock electromagnetic radiation phase space was reconstructed through Takens theory, and time delay and embedding dimension are determined by mutual information method and false nearest neighbor method respectively. Then, the forecasting model of coal rock electromagnetic radiation was constructed via ANFIS in the reconstruction phase space, and the parameters of ANFIS are tuned by hybrid learning algorithm. Finally, the simulation results and comparison analysis are presented, the training and checking root mean squared error are 0.0248 and 0.0286 respectively, which indicates that the ANFIS has better learning ability and generalization performance, thus, the model is creditable and feasible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yu, Q.X.: Coal Mine Gas Control. China University of Mining and Technology Press, Xuzhou (1992)
Dou, L.M., He, X.Q., Xueqiu, H.E.: Rock Burst Control Theory and Technology. China University of Mining and Technology Press, Xuzhou (2001)
He, X.Q., Wang, E.Y., Nie, B.S.: Coal Electric Magnetic Dynamics. Science Press, Beijing (2003)
Jang, J.S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference System. IEEE Transactions on System, Man and Cybernetics 23, 665–685 (1993)
Taklens, F.: Detecting Strange Attractors in Turbulence. Lecture Notes in Mathematics, vol. 898, pp. 361–381 (1981)
Packard, N.H., Crutchfied, J.P., Farmer, J.D., Shaw, R.S.: Geometry from a Time Series. Physical Review Letters 45, 712–716 (1980)
Wang, H.Y., Sheng, Z.H.: Choice of the Parameters for the Phase Space Reconstruction of Chaotic Time Series. Journal of Southeast University: Natural Science Edition 30, 113–117 (2000)
Wang, Y., Xu, W., Qu, J.S.: The Algorithm and Chick of Phase-Space Reconstruction Based on The Time Series. Journal of Shandong University: Engineering Science 35, 109–114 (2005)
Lee, S.J., Ouyang, C.S.: A Neuro-Fuzzy System Modeling With Self-Constructing Rule Generation and Hybrid SVD-based Learning. IEEE Transactions on Fuzzy Systems 11, 341–353 (2003)
Cheng, J., Guo, Y.N., Qian, J.S.: Estimation of Loose Status of Jigging bed based on Adaptive Neuro-Fuzzy Inference System. Journal of China University of Mining & Technology: English Edition 16, 270–274 (2006)
Panella, M., Gallo, A.S.: An Input-Output Clustering Approach to The Synthesis of ANFIS Networks. IEEE Transactions on Fuzzy Systems 13, 69–81 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, J., Cheng, J., Li, L. (2010). Forecasting Coal and Rock Dynamic Disaster Based on Adaptive Neuro-Fuzzy Inference System. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16732-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-16732-4_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16731-7
Online ISBN: 978-3-642-16732-4
eBook Packages: Computer ScienceComputer Science (R0)