Intelligent Control Systems using Computational Intelligence Techniques

References

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  3. Nelles, O.: Nonlinear systems identification: from classical approaches to neural networks and fuzzy models (Springer-Verlag, Berlin, 2000)

  4. Brown, M., and Harris, C. J.: Neurofuzzy adaptive modelling and control (Prentice-Hall, Hemel Hempstead, 1994)

  5. Harris, C., Hong, X., and Gan, Q.: Adaptive modelling, estimation and fusion from data: a neurofuzzy approach (Springer-Verlag, Berlin, 2002)

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  8. Hornik, K., Stinchcombe, M., and White, H.: Multilayer feed- forward networks are universal approximators , Neural Networks, 1989, 2, pp. 359 66

  9. Broomhead, D., and Lowe, D.: Multivariable functional interpolation and adaptive networks , Complex Systems, 1988, 2, pp. 321 55

  10. Girosi, F., and Poggio, T.: Networks and the best approximation property , Biological Cybernetics, 1990, 63, pp. 169 76

  11. Werbos, P.: Beyond regression: new tools for prediction and analysis in the behavioral sciences . Doctoral dissertation, Applied Mathematics, Harvard University, USA, 1974

  12. Watrous, R.: Learning algorithms for connectionist networks: applied gradient methods of nonlinear optimization . Proceedings of first IEEE international conference on Neural networks, San Diego, USA, 1987, vol. 2, pp. 619 27

  13. Jacobs, R.: Increased rates of convergence through learning...

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