[1] C. J. Harris, X. Hong, and Q. Gan, Adaptive Modelling, Estimation and
Fusion from Data: A Neurofuzzy Approach, Springer-Verlag, 2002.
[2] M. Brown and C. J. Harris, Neurofuzzy Adaptive Modelling and Control,
Prentice Hall, Hemel Hempstead, 1994.
[3] A. E. Ruano, Intelligent Control Systems using Computational Intelligence
Techniques, IEE Publishing, 2005.
[4] R. Murray-Smith and T. A. Johansen, Multiple Model Approaches to
Modelling and Control, Taylor and Francis, 1997.
[5] S. G. Fabri and V. Kadirkamanathan, Functional Adaptive Control: An
Intelligent Systems Approach, Springer, 2001.
[6] M. Stone, “Cross validatory choie and assessment of statistical predictions,”
Journal of the Royal Statistical Society, Series B, vol. 36, pp.
117–147, 1974.
[7] S. Chen, Y. Wu, and B. L. Luk, “Combined genetic algorithm
optimization and regularized orthogonal least squares learning for radial
basis function networks,” IEEE Trans. on Neural Networks, vol. 10, pp.
1239–1243, 1999.
[8] M. J. L. Orr, “Regularisation in the selection of radial basis function
centers,” Neural Computation, vol. 7, no. 3, pp. 954–975, 1995.
[9] X. Hong and Billings, “Parameter estimation based on stacked regression
and evolutionary algorithms,” IEE Proc. - Control Theory and
Applications, vol. 146, no. 5, pp. 406–414, 1998.
[10] L. Ljung and T. Glad, Modelling of Dynamic Systems, Prentice Hall,
Englewood Cliffs, NJ, 1994.
[11] R. H. Myers, Classical and modern regression with applications, PWSKENT,
Boston, 2nd edn., 1990.
[12] S. Chen, S. A. Billings, and W. Luo, “Orthogonal least squares methods
and their applications to non-linear system identification,” International
Journal of Control, vol. 50, pp. 1873–1896, 1989.
[13] M. J. Korenberg, “Identifying nonlinear difference equation and functional
expansion representations: the fast orthogonal algorithm,” Annals
of Biomedical Engineering, vol. 16, pp. 123–142, 1988.
[14] L. Wang and J. M. Mendel, “Fuzzy basis functions, universal approximation,
and orthogonal least-squares learning,” IEEE Trans. on Neural
Networks, vol. 5, pp. 807–814, 1992.
[15] X. Hong and C. J. Harris, “Neurofuzzy design and model construction of
nonlinear dynamical processes from data,” IEE Proc. - Control Theory
and Applications, vol. 148, no. 6, pp. 530–538, 2001.
[16] Q. Zhang, “Using wavelets network in nonparametric estimation,” IEEE
Trans. on Neural Networks, vol. 8, no. 2, pp. 1997, 1993.
[17] S. A. Billings and H. L. Wei, “The wavelet-narmax representation: A
hybrid model structure combining polynomial models with multiresolution
wavelet decompositions,” International Journal of Systems Science,
vol. 36, no. 3, pp. 137 – 152, 2005.
[18] X. Hong, P. M. Sharkey, and K. Warwick, “Automatic nonlinear
predictive model construction using forward regression and the PRESS
statistic,” IEE Proc.-Control Theory Appl., vol. 150, no. 3, pp. 245–254,
2003.
[19] S. Chen, X. Hong, and C. J. Harris, “Sparse kernel regression modelling
using combined locally regularised orthogonal least squares and Doptimality
experimental design,” IEEE Trans. on Automatic Control,
vol. 48, no. 6, pp. 1029–1036, 2003.
[20] H. Zou and T. Hastie, “Regularization and variable selection via the
elastic net,” J. R. Stasti. Soc. B, vol. 67, no. 2, pp. 301–320, 2005.
[21] S. Chen, “Locally regularised orthogonal least squares algorithm for the
construction of sparse kernel regression models,” in Proceedings of 6th
Int. Cof. Signal Processing, Beijing, China, 2002, pp. 1229–1232.
[22] D. J. C. MacKay, Bayesian Methods for Adaptive Models, Ph.D. thesis,
California Institute of Technology, USA, 1991.
[23] S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition
by basis pursuit,” SIAM Journal on Scientific Computing, vol. 20, no.
1, pp. 33–61, 1998.
[24] R. Tibshirani, “Regression shrinkage and selection via the lasso,”
Journal of Royal Statistical Society. Series B, vol. 58, no. 1, pp. 267–
288, 1996.
[25] B. Efron, I. Johnstone, T. Hastie, and R. Tibshirani, “Least angle
regression,” Annals of Statistics, vol. 32, pp. 407–451, 2004.
[26] J. Friedman, T. Hastie, and R Tibshirami, “Regularization paths for
generalized linear models via coordinate descent,” Journal of Statistical
Software, vol. 33, no. 1, pp. 1–22, 2010.
[27] J. Friedman, T. Hastie, H. Hofling, and R. Tibshirami, “Pathwise
coordinate descent,” The Annals of Statistics, vol. 1, no. 2, pp. 302–332,
2007.