Abstract
This paper proposes a novel high-order associative memory system (AMS) based on the Newton’s forward interpolation (NFI), The Interpolation Polynomials and training algorithms for the new AMS scheme are derived. The proposed novel AMS is capable of implementing error-free approximation to complex nonlinear functions of arbitrary order. A method Based on NFI-AMS is designed to model and predict network traffic dynamics, which is capable of modeling the complex nonlinear behavior of a traffic time series and capturing the properties of network traffic. The simulation results showed that the proposed scheme is feasible and efficient. Furthermore, the NFI-AMS based traffic prediction can be used in more fields for network design, management and control.
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References
Wang, J.S.: Associative Memory Systems-based Robot Intelligent Control System. Bei-Jing Poly-technique University Master Paper (1998)
Albus, J.S.: A New Approach to Manipulator Control: The Cerebella Model Articulation Controller (CMAC). Trans. ASME, Series G, J. Dynamics Systems, Measurement, Control 97, 220–227 (1975)
Xu, N.S., Wu, Z.L., Jia, R.X., Zhang, H.: A New Content-addressing Mechanism of CMAC-type Associative Memory Systems for Reducing the Required Memory Size. In: Proc. 13th IFAC World Congr., pp. 357–362 (1996)
Thompson, D.E., Sunggyu, K.: Neighborhood Sequential and Random Training Techniques for CMAC. IEEE Trans. Neural Networks 6, 196–202 (1995)
Gonzalez-Serrano, F.J., Figueiras-Vidal, A.R., Artes-Rodriguez, A.: Generalizing CMAC Architecture and Training. IEEE Trans. Neural Networks 9, 1509–1514 (1998)
Wang, Z.Q., Schiano, J.L., Ginsberg, M.: Hash-coding in CMAC Neural Networks. Proc. IEEE Int. Conf. Neural Networks 3, 1698–1703 (1996)
Xu, N.S., Bai, Y.F., Zhang, L.: A Novel High-order Associative Memory System via Discrete Taylor Series. IEEE Trans. Neural Networks 14, 734–747 (2003)
Shu, Y.T., Wang, L., Zang, L.F.: Internet Traffic Modeling and Prediction Using FARIMA Model. Chinese Journal of Computers 24(1), 46–54 (2001)
Xu, N.S., Bai, Y.F., Lin, Q.: Model Parameter Estimation Based on Associative Memory System. J. Beijing Polytech. Univ. 22(4), 134–143 (1996)
Xu, N.S., Wang, J.S., Feng, W.N.: Associative Memory-based Robotic Manipulator Intelligent Control System. In: Proc. 19th Chinese Control Conf., Hong Kong, pp. 558–563 (2000)
Chen, H.L., Liu, Z.X., Chen, Z.Q., Yuan, Z.Z.: Estimating TCP Throughput: A Neural Network Approach. In: Proc 6th World Congress on Control and Automation, Dalian, pp. 2850–2854 (2006)
Douligerisa, C., Singhb, B.K.: Analysis of neural-network-based congestion control algorithms for ATM networks. Engineering Applications of Artificial Intelligence 12, 453–470 (1999)
Hyun, C., Cho, M., Fadali, S., Lee, H.: Neural Network Control for TCP Network Congestion. In: 2005 American Control Conference, Portland, OR, USA, pp. 3480–3485 (2005)
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Wang, JS., Gao, ZW., Xu, NS. (2007). A Novel Associative Memory System Based Modeling and Prediction of TCP Network Traffic. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_62
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DOI: https://doi.org/10.1007/978-3-540-72383-7_62
Publisher Name: Springer, Berlin, Heidelberg
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