Abstract
Data extrapolation in FDTD simulations using feedforward multilayer Perceptron (MLP) showed promising results in a previous study. This work studies two different aspects of the problem: First is the learning aspect, including the effect of prior training with the same class of random signals, which is an attempt to find a general solution to the weight initialization problem in adaptive systems. The second aspect covers the steps to make the extrapolator fully adaptive, through optimization of the time step sensitivity and the input layer width of a sliding window extrapolator. Average mutual information is used as a performance measure in most of the work.
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References
Ababarnel, H. D. I., Brown, R., Sidorowich, J. L., Tsimring, L. S.: The analysis of observed chaotic data in physical systems. Rev. of Modern Physics. Vol. 65 (1993) 1331–1392
Göksu, H., Selli, G., Wunsch, D. C. II.: FDTD data extrapolation using multilayer perceptron (MLP). IEEE Symp. EMC 2003-Boston. (2003) Accepted.
Wu, C., Navarro, A., Litva, J.: Combination of finite impulse response neural network technique with FDTD method for simulation of electromagnetic problems. Electron. Lett., Vol. 32. (1996) 1112–1113
Narendra, K., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. Trans. Neural Networks, Vol. 78(1990) 4–27
Taflove, A., Umashankar, K. R.: Review of FDTD numerical modeling of electromagnetic wave scattering and radar cross section. Proc. IEEE, Vol. 77(1989) 682–699
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Göksu, H., C., D. (2003). Neural Networks Applied to Electromagnetic Compatibility (EMC) Simulations. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_126
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DOI: https://doi.org/10.1007/3-540-44989-2_126
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