Abstract
Grey neural network is an innovative intelligent computing approach combing grey system model and neural network, which makes full use of the similarities and complementarity between grey system model and neural network to settle the disadvantage of applying Grey model and Neural Network separately. Some optimization algorithms such as genetic algorithm are also employed to modeling and optimizaion of grey neural network. Many typical grey neural work models such as GNNM(1,1),GRBF,DGRBF, GA-GRBF and so on are proposed and applied in this paper. A lot of comparative experimental results show that grey neural network models are capable of predicting a small sample of data accurately, easily and conveniently. The key technologies, research hotspots, difficulties and further development of grey neural network are discussed in this paper.
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References
Yuan, J., Zhong, L., Tong, Q., et al.: The Modeling of Metabolic GM(1,1) Prediction Model and Its Application. In: Progress in Intelligence Computation and Application, ISICA 2005, pp. 718–722 (2005)
Yuan, J., Zhong, L., Jiang, Q.: A Study on Grey RBF Prediction Model. In: Yeung, D.S., Liu, Z.-Q., Wang, X.-Z., Yan, H. (eds.) ICMLC 2005. LNCS, vol. 3930, pp. 4140–4143. Springer, Heidelberg (2006)
Yuan, J., Zhong, L.: The Dynamic Grey Radial Basis Function Prediction Model and its Applications. In: Proceeding of IEEE ICICIC 2006, pp. 582–585 (2006)
Liu, K.: Mathematic process and application of uncertain information. Science Press house, Beijing (1999)
Gao, W., Feng, X.: Study on displacement prediction of landslide based on grey system and evolutionary neural network. Rock and Soil Mechanics 25(5), 514–517 (2004)
Ruan, P., Lei, Z., Wang, H.: Long-term power load prediction based on grey system and neural network. Computer applications 24(6), 285–286 (2004)
Zhong, L., Rao, W.: Neural network and its fusion technology. Science Press house, Beijing (2007)
Lu, H., Zhang, Y., Yu, Q., Li, H.: Grey forecasting model based on evolving neural network. Journal of PLA University of Science and Technology(Natural Edition) 7(5), 437–441 (2006)
Chen, S., Chen, J.: Application of a novel model to Traffic Flow Prediction. Journal of highway and transportation research and development 21(2), 80–83 (2004)
Zhong, L., Liu, L., Zou, C., Yuan, J.: The Application of Neural Network in Lifetime Prediction of Concrete. Journal of Wuhan University of Technology 17(1), 79–81 (2002)
Zhong, L., Yuan, J., Xia, H., et al.: A Study on Gray Neural Network Modeling. In: Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, China, pp. 2021–2023 (2002)
Lv, H., Zhong, L., Xia, H.: Exploring in Fusion Technology of Grey system and neural networks. Micro-Computer development 10(3), 3–5 (2000)
Zhong, L., Bai, Z., Xia, H., Zhou, X.: Optimization and Application of Neural Network Modeling for Gray Problem. Computer project and Applications 37(8), 33–34 (2001)
Zhong, L., Bai, Z., Xia, H.: Whitening method of grey neural network modeling. Pattern Recognition and Artificial Intelligence 14(2), 145–149 (2001)
Shang, G., Zhong, L., Yan, J.: Establishment and Application of two Grey neural network model. Journal of Wuhan University of Technology 24(2), 78–81 (2002)
Zhong, L., Zhou, H.: Analysis of Oxidation heat treatment conditions’ effect to electric properties of ceramics doping SrTiO3 based on grey neural network. Journal Wuhan University of Technology 26(1), 22–24 (2004)
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Yuan, J., Zhong, L., Li, X., Li, J. (2008). Modeling of Grey Neural Network and Its Applications. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_33
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DOI: https://doi.org/10.1007/978-3-540-92137-0_33
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