J. C. Principe, L. Wang, and M. A. Motter, "Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control," Proceedings of the IEEE, vol. 86, no. 11, pp. 2240--2258, 1998. Home/Search Document Details and Download
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Modeling Nonlinear Dynamics with Extended Kalman Filter TRAINED.. - Patel (Correct)
....[u(n Gamma 1) u(n Gamma M ) is the input to the network and the parameters to be determined are the centers t n Gammai and weights w i , i = 1 : N , as well as the width oe(n) 2.2. 4 SOM Based Locally Linear Models Self organizing maps (SOM) were used by Principe, Wang and Motter in [2, 17] for the purpose of dynamic modeling. In this approach, a finite set of SOM based local linear models are developed to approximate the global dynamics. The modeling is accomplished in three stages: first, the input time series is embedded in a reconstruction space, second, the embedded vectors are ....
....SOM learning scheme and then at the final stage a local linear approximation of the local dynamics is performed around the winning neuron by a least squares estimation procedure. Wang [2] also proposed a dynamic learning scheme which is really a modification to the basic Kohonen s learning rule [2, 17]. In the dynamic learning scheme, the learning rate is controlled by the instantaneous prediction error at each step. Thus, the learning rate is higher at difficult areas where there is a larger prediction error. This SOM based method CHAPTER 2. DYNAMIC SYSTEMS AND MODELING 16 produced reasonable ....
Jose C. Principe, Ludong Wang, and Mark A. Motter, "Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control", in Proceedings of the IEEE, vol. 86, pp. 2240--2258, Nov. 1998.
Mark A. Motter Flight Dynamics and Control Division NASA Langley .. - Na Sa Gov (1999) Self-citation (Motter) (Correct)
....finite number of models are used to cover a broad range of system dynamics, coverage of the full dynamical space becomes an issue. The Kohonen self organizing map (SOM) 1] is employed as the basis for dynamic modeling and extended to a control framework, where the modeled system is nonautonomous [4]. The idea here is that the SOM , trained with responses from the full operating range, provides a basis for local dynamic models that fully cover the dynamical space corresponding to a representative or prototype control. For the application, we were able to cluster the inputs onto a small set of ....
J.C. Principe, L. Wang, and M. Motter, "Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control", in Proceedings of the IEEE, Vol. 86, No. 11, 1998. Criteria Existing Operator PMMSC % Out of tol 329 s 310 s 266 s 19 / 17 L1 [u] 424 466 374 12 / 20
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J. C. Principe, L. Wang, and M. A. Motter, "Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control," Proceedings of the IEEE, vol. 86, no. 11, pp. 2240--2258, 1998.
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J. C. Principe, L. Wang, and M. A. Motter, Local dynamic modeling with self--organizing maps and applications to nonlinear system identification and control, Proceedings of the IEEE 86 (1998), no. 11, 2240--2258.
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J. C. Principe, L. Wang, and M. A. Motter, Local dynamic modeling with self--organizing maps and applications to nonlinear system identification and control, Proceedings of the IEEE 86 (1998), no. 11, 2240--2258.
Computational Intelligence Methods for Financial.. - Pavlidis.. (2005) (Correct)
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Principe, J. C., Wang, L. & Motter, M. A. [1998] "Local dynamic modeling with self--organizing maps and applications to nonlinear system identification and control", Proceedings of the IEEE, 86(11), 2240--2258.
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J. C. Principe, L. Wang, and M. A. Motter. Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control. Proceedings of the IEEE, 86(11):2240--2258, 1998.
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J. Principe, L. Wang, and M. Motter, "Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control," Proceedings of the IEEE, vol. 86, no. 11, pp. 2240--2258, 1998.
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J. C. Principe, L. Wang, and M. A. Motter, Local dynamic modeling with self--organizing maps and applications to nonlinear system identification and control, Proceedings of the IEEE, no. 6, 1998, pp. 2240--2257.
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Principe, J. C., Wang, L., and Motter, M. A. (1998). Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control. Proceedings of the IEEE, 86(11):2240--58.
Time Series Forecasting Methodology for.. - Pavlidis, Tasoulis.. (2004) (Correct)
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J. C. Principe, L. Wang, and M. A. Motter, Local dynamic modeling with self--organizing maps and applications to nonlinear system identification and control, Proceedings of the IEEE, no. 6, 1998, pp. 2240--2257.
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