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Application of Neural Networks for Predictive and Control Purposes

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Abstract

For a company to stay competitive, providing “intelligent” application solutions, services and products to its customers is indispensable. For example, forthcoming telematics applications require technologies like adaptive control, model building by learning, and focusing attention on relevant features. Neural Networks offer appropriate architectures for these purposes. This contribution gives an overview on activities at Siemens Corporate Research using Neural Networks for prediction and control purposes.

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References

  1. R. Neuneier and H.G. Zimmermann, How to Train Neural Networks, In: Neural Networks: Tricks of the Trade, G.B. Orr and K.-R. Miiller, (Eds.), pp. 373–423 (Springer, Berlin, 1998).

    Chapter  Google Scholar 

  2. R. Sollacher and P. Konhäuser, SANDY - Nichtlineare Dynamik im Strafienverkehr, in: Statusseminar Technische Anwendungen von Erkenntnissen der Nichtlinearen Dynamik, pp. 103-112 (VDI Technologiezentrum Physikalische Technologien, Düsseldorf, 1999).

    Google Scholar 

  3. R. Sollacher, Ch. Stutz, and H. Lenz, Prognose und Steuerung von Verkehr, in: Statusseminar Technische Anwendungen von Erkenntnissen der Nichtlinearen Dynamik, pp. 177-180 (VDI Technologiezentrum Physikalische Technologien, Düsseldorf, 1999).

    Google Scholar 

  4. R. Sollacher and H. Lenz Nonlinear Control of Stop-and-Go Traffic, in: These proceedings.

    Google Scholar 

  5. C. Wagner, C. Hoffmann, R. Sollacher, J. Wagenhuber, and B. Schürmann, Second-Order Continuum Traffic Flow Model, Phys. Rev. E 54, 5073-5085 (1996).

    Google Scholar 

  6. H. Lenz, Ch. Wagner, and R. Sollacher, Multi-anticipative car-following model, Eur. Phys. J.B 7, 331–335 (1999).

    Google Scholar 

  7. F. Black and R. Litterman, Global Portfolio Optimization, Fin. Analysts J., Sep., (1992).

    Google Scholar 

  8. H.M. Markowitz, Portfolio Selection, J. of Finance 7, 77–91 (1952).

    Article  Google Scholar 

  9. W.F. Sharpe, Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk, J. of Finance, Sep., (1964).

    Google Scholar 

  10. J. Lintner, The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets, Rev. of Eco. and Stat., Feb., (1965).

    Google Scholar 

  11. H.G. Zimmermann and R. Neuneier, Active Portfolio-Management with Neural Networks, in: Proc. of Comp. Finance CF’1999, A.S. Weigend and B. LeBaron, (Eds.), (Springer, 1999).

    Google Scholar 

  12. D. Bertsekas and J.N. Tsitsiklis,Neuro-Dynamic Programming, (Athena Scientific, Melmont, Massachusetts, 1996)

    MATH  Google Scholar 

  13. P. Marbach, O. Mihatsch, and J.N. Tsitsiklis,Call Admission Control and Routing in Integrated Services Networks Using Neuro-Dynamic Programming, to appear in IEEEJ. on Selected Areas in Communic., (2000).

    Google Scholar 

  14. M. Appl and R. Palm, Fuzzy Q-Learning in Nonstationary Environments, in: Proc. of the 7th Eur. Congress on Intelligent Techniques & Soft Computing (EUFIT’ 99), (1999).

    Google Scholar 

  15. G. Deco and B. Schürmann, Spatiotemporal Coding in the Cortex: Information Flow-based Learning in spiking Neural Networks, Neural Comp. 11, 919–934 (1999).

    Article  Google Scholar 

  16. G. Deco and B. Schürmann, AN euro-Cognitive Visual System for Object Recognition based on Interactive Attentional Top-Down Hypothesis Testing, submitted, Perception, (1999).

    Google Scholar 

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© 2000 Springer-Verlag Berlin Heidelberg

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Schürmann, B. (2000). Application of Neural Networks for Predictive and Control Purposes. In: Helbing, D., Herrmann, H.J., Schreckenberg, M., Wolf, D.E. (eds) Traffic and Granular Flow ’99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59751-0_17

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  • DOI: https://doi.org/10.1007/978-3-642-59751-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64109-1

  • Online ISBN: 978-3-642-59751-0

  • eBook Packages: Springer Book Archive

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