Abstract
With ore body space interpolation and the three dimensional simulation visualization, has been for the internal structure of the ore body is too complex and the effect not beautiful. Using nonlinear improved ant colony radial basis neural network method for ore grade for interpolation, in contrast to the traditional inverse distance square interpolation method to improve the precision of more. With Vc++ and OpenGL environment developed ore body visualization software, and ore body grade distribution visualization, facilitate further research.
Keywords
- Spatial Interpolation Model
- Radial Basis Function Neural Network
- Inverse Distance Method
- OpenGL Environment
- Orebodies
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Houlding, S.: 3D Geoscience Modeling: Computer Techniques for Geological Characterization. Springer, London (1994)
Er, M.J., Wu, S., Lu, J., et al.: Face recognition with radial basis function (RBF) neural networks. IEEE Trans. Neural Networks 13(3), 697–710 (2002)
Seshagiri, S., Khalil, H.K.: Output feedback control of nonlinear systems using RBF neural networks. IEEE Trans. Neural Networks 11(1), 69–79 (2000)
Yingwei, L., Sundararajan, N., Saratchandran, P.: Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans. Neural Networks 9(2), 308–318 (1998)
Wedding, D.K., Cios, K.J.: Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model. Neurocomputing 10(2), 149–168 (1996)
Li, Y., Qiang, S., Zhuang, X., et al.: Robust and adaptive backstepping control for nonlinear systems using RBF neural networks. IEEE Trans. Neural Networks 15(3), 693–701 (2004)
Yun, Z., Quan, Z., Caixin, S., et al.: RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans. Power Syst. 23(3), 853–858 (2008)
Yang, F., Paindavoine, M.: Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification. IEEE Trans. Neural Networks 14(5), 1162–1175 (2003)
Chung, K.M., Kao, W.C., Sun, C.L., et al.: Radius margin bounds for support vector machines with the RBF kernel. Neural Comput. 15(11), 2643–2681 (2003)
Funahashi, K.J.: On the approximate realization of continuous mapping by neural networks. Neural Net-works 2, 183–192 (1989)
Skaf, Z., Wang, H., Guo, L.: Fault tolerant control based on stochastic distribution via RBF neural networks. J. Syst. Eng. Electron. 1, 63–69 (2011)
Thibault, J.: Feedforward neural networks for the identification of dynamic process. Chem. Eng. Commun. 105, 109–128 (1991)
Wang, H., Afshar, P., Yue, H.: ILC-based generalised PI control for output PDF of stochastic systems using LMI and RBF neural networks. In: Proceedings of the IEEE Conference on Decision and Control, pp. 5048–5053 (2006)
Gutjahr, W.J.: A graph-based ant system and its convergence. Future Gener. Comput. Syst. 16(8), 873–888 (2000)
Derigo, M., Di Caro, G.: Ant algorithms for discrete optimization. Artif. Life 5(3), 137–172 (1999)
Stutzle, T., Hoos, H.H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)
Acknowledgement
This work is supported by Guangxi Key Laboratory of Cryptography and Information Security, Grant/Award Number: GCIS201610
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Chen, X., Wang, X., Wu, X. (2018). Improved Ant Colony RBF Spatial Interpolation of Ore Body Visualization Software Development. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-69835-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69834-2
Online ISBN: 978-3-319-69835-9
eBook Packages: EngineeringEngineering (R0)