References
- Akaike , H. 1969 . Fitting autoregressive models for prediction . Ann. Inst. Stat. Math. , 20 : 425 – 439 .
- Corchado , E. and Fyfe , C. 2003 . Connectionist techniques for the identification and suppression of interfering underlying factors . Int. J. Pattern Recogn. Artif. Intell. , 17 ( 8 ) : 1447 – 1466 .
- Corchado , E. , Han , Y. and Fyfe , C. 2003 . Structuring global responses of local filters using lateral connections . J. Experiment. Theoret. Artif. Intell. , 15 ( 4 ) : 473 – 487 .
- Corchado , E. , MacDonald , D. and Fyfe , C. 2004 . Maximum and minimum likelihood Hebbian learning for exploratory projection pursuit . Data Mining Knowl. Discov. , 8 ( 3 ) : 203 – 225 .
- Diaconis , P. and Freedman , D. 1984 . Asymptotics of graphical projections . Ann. Statist. , 12 ( 3 ) : 793 – 815 .
- Friedman , J. H. and Tukey , J. W. 1974 . Projection pursuit algorithm for exploratory data-analysis . IEEE Trans. Comput. , 23 ( 9 ) : 881 – 890 .
- Fyfe , C. 1993 . PCA Properties of Interneurons: From Neurobiology to Real World Computing , Vol. 93 , 183 – 188 . Amsterdam, , The Netherlands : Springer-Verlag . Proceedings of the International Conference on Artificial Neural Networks (ICANN 1993)
- Fyfe , C. and Corchado , E. 2002 . Maximum Likelihood Hebbian Rules , 143 – 148 . Bruges, , Belgium : d-side Publishers . Proceedings of the 10th European Symposium on Artificial Neural Networks (ESANN 2002)
- Haber , R. and Keviczky , L. 1999 . Nonlinear System Identification, Input–Output Modeling Approach, Part 2: Nonlinear System Structure Identification , Dordrecht, , The Netherlands : Kluwer Academic Publishers .
- Haber , R. and Keviczky , L. 1999 . Nonlinear System Identification, Input–Output Modeling Approach, Part 1: Nonlinear System Parameter Estimation , Dordrecht, , The Netherlands : Kluwer Academic Publishers .
- He , X. and Asada , H. 1993 . Proceedings of the American Control Conference . 1993 , S. F, California. A New Method for Identifying Orders of Input–Output Models for Nonlinear Dynamic Systems , pp. 2520 – 2523 .
- Hotelling , H. 1933 . Analysis of a complex of statistical variables into principal components . J. Educ. Psychol. , 24 : 417 – 444 .
- Ljung , L. 1999 . System Identification, Theory for the User, , 2. , Upper Saddle River, , New Jersey, USA : Prentice-Hall .
- Lorenzo , M. , Bravo , P. and Preciado , M. 2004 . “ Parametrización del Corte en Hormigón Fuertemente Armado Mediante Coronas de Diamante ” . Spain IX Congreso Nacional de Propiedades Mecánicas de Sólidos
- Nelles , O. 2001 . Nonlinear System Identification, from Classical Approaches to Neural Networks and Fuzzy Models , Springer .
- Nögaard , M. , Ravn , O. , Poulsen , N. K. and Hansen , L. K. 2000 . Neural Networks for Modelling and Control of Dynamic Systems , London, , UK : Springer-Verlag .
- Oja , E. 1982 . A simplified neuron model as a principal component analyzer . J. Math. Biol. , 15 ( 3 ) : 267 – 273 .
- Pearson , K. 1901 . On lines and planes of closest fit to systems of points in space . Philos. Mag. , 2 ( 6 ) : 559 – 572 .
- Seung , H. S. , Socci , N. D. and Lee , D. 1998 . The rectified Gaussian distribution . Adv. Neural Inf. Processing Syst. , 10 : 350 – 356 .
- Söderström , T. and Stoica , P. 1989 . System Identification , London, , UK : Prentice Hall International .
- Stoica , P. and Söderström , T. 1982 . A useful parametrization for optimal experimental design . IEEE Trans. Automatic. Control , AC-27