Nonlinear Input/Output Modeling

[1]  H. Davis.  Introduction to Nonlinear Differential and Integral Equations , 1964 .

[2]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[3]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[4]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[5]  James R. Munkres,et al.  Topology; a first course , 1974 .

[6]  G. Klambauer.  Mathematical Analysis , 1975 .

[7]  D. Brillinger.  The identification of a particular nonlinear time series system , 1977 .

[8]  Yakov Z. Tsypkin,et al.  Robust identification , 1980, Autom..

[9]  Stephen A. Billings,et al.  Identi cation of nonlinear systems-A survey , 1980 .

[10]  D. Thomson,et al.  Robust-resistant spectrum estimation , 1982, Proceedings of the IEEE.

[11]  Taiho Koh,et al.  Second-order Volterra filtering and its application to nonlinear system identification , 1985, IEEE Trans. Acoust. Speech Signal Process..

[12]  S. Billings,et al.  A prediction-error and stepwise-regression estimation algorithm for non-linear systems , 1986 .

[13]  Evanghelos Zafiriou,et al.  Robust process control , 1987 .

[14]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[15]  S. Monaco,et al.  Zero dynamics of sampled nonlinear systems , 1988 .

[16]  Miroslaw Pawlak,et al.  Nonparametric identification of Hammerstein systems , 1989, IEEE Trans. Inf. Theory.

[17]  John P. Congalidis,et al.  Feedforward and feedback control of a solution copolymerization reactor , 1989 .

[18]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[19]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[20]  W. Härdle.  Applied Nonparametric Regression , 1991 .

[21]  J. Bendat.  New techniques for nonlinear system analysis and identification from random data , 1990 .

[22]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[23]  Howell Tong,et al.  Non-Linear Time Series , 1990 .

[24]  Heinz Unbehauen,et al.  Structure identification of nonlinear dynamic systems - A survey on input/output approaches , 1990, Autom..

[25]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[26]  Stanley H. Johnson,et al.  Use of Hammerstein Models in Identification of Nonlinear Systems , 1991 .

[27]  E. J. Powers,et al.  Estimation of quadratically nonlinear systems with an i.i.d. input , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[28]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[29]  Martin Pottman,et al.  Identification of non-linear processes using reciprocal multiquadric functions , 1992 .

[30]  Dale E. Seborg,et al.  Nonlinear internal model control strategy for neural network models , 1992 .

[31]  Wlodzimierz Greblicki,et al.  Nonparametric identification of Wiener systems , 1992, IEEE Trans. Inf. Theory.

[32]  T. McAvoy,et al.  Integration of multilayer perceptron networks and linear dynamic models : a Hammerstein modeling approach , 1993 .

[33]  Ruey S. Tsay,et al.  Nonlinear Additive ARX Models , 1993 .

[34]  Naomi S. Altman,et al.  Assessing Influence in Variable Selection Problems , 1993 .

[35]  Dale E. Seborg,et al.  Application of a general multi-model approach for identification of highly nonlinear processes-a case study , 1993 .

[36]  Yaman Arkun,et al.  Control of nonlinear systems using polynomial ARMA models , 1993 .

[37]  W. Harmon Ray,et al.  Creating efficient nonlinear neural network process models that allow model interpretation , 1993 .