Abed-meraim K, Qui WZ, Hua YB. (1997). Blind system identification Proc IEEE. 85

Bazaraa MS, Sherali HD, Shetty CM. (1993). Nonlinear programming theory and algorithms (2nd ed).

Chan H, Xia YS, Leung H. (2006). A prediction fusion method for reconstructing spatial temporal dynamics using support vector machines IEEE Trans Circuits And Systems part II Express Briefs. 53

Chang CC, Lin CJ, Hsu CW. (2003). A practical guide to support vector classification Tech Rep Department of Computer Science and Information Engineering, National Taiwan University.

Chen L, Narendra KS. (2004). Identification and control of a nonlinear discrete-time system based on its linearization: a unified framework. IEEE transactions on neural networks. 15 [PubMed]

Chen S, Billings SA. (1992). Neural networks for nonlinear dynamic system modelling and identification Intl Journal Of Control. 56

Cressie N, Majure JJ. (1997). Spatial-temporal statistical modeling of live-stock waste in streams Journal of Agricultural, Biological, and Environmental Statistics. 2

Cressie N, Wikle CK. (1999). A dimension-reduced approach to space-time Kalman filtering Biometrika. 86

Drosopoulos A. (1994). Description of the OHGR database Tech Note 94-14 Ottawa Defence Research Establishment.

Fan J, Gijbels I. (1996). Local polynomial modelling and its application.

Girosi F, Mukherjee S, Osuna E. (1997). Nonlinear prediction of chaotic time series using support vector machines Proc IEEE NNSP.

Hall DL, Llinas J. (1997). An introduction to multisensor data fusion Proc IEEE. 85

Hardle W. (1990). Applied nonparametric regression.

Haykin S, Puthusserypady S. (1997). Chaotic dynamics of sea clutter. Chaos (Woodbury, N.Y.). 7 [PubMed]

Hornik K, Stinhcombe M, White H. (1989). Multilayer feedforward networks are universal approximators Neural Networks. 2

Kalouptsidis N, Koukoulas P. (2000). Second-order Volterra system identification IEEE Trans Signal Processing. 48

Kinderlehrer D, Stampacchia G. (1980). An introduction to variational inequalities and their applications.

Lee J, Mathews VJ. (1993). A fast recursive least squares adaptive second-order Volterra filter and its performance analysis IEEE Trans Signal Processing. 41

Leung H, Hennessey G, Drosopoulos A. (2000). Signal detection using the radial basis function coupled map lattice. IEEE transactions on neural networks. 11 [PubMed]

Ljung L. (1999). System identification: Theory for the user (2nd ed).

Ljung L, Roll J, Nazin A. (2005). Nonlinear system identification via direct weight optimization Automatica. 41

Ljung L et al. (1995). Nonlinear black-box modeling in system identification: A unified overview Automatica. 31

Lu S, Ju KH, Chon KH. (2001). A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry. IEEE transactions on bio-medical engineering. 48 [PubMed]

Mardia KV, Goodall C, Redfern EJ, Alsonso FJ. (1998). The kriged Kalman filter Test. 7

Mardia KV, Sahu SK. (1999). A Bayesian kriged Kalman model for short-term forecasting of air pollution levels Applied Statistics. 54

Mhaskar HN, Hahm N. (1997). Neural networks for functional approximation and system identification. Neural computation. 9 [PubMed]

Narendra KS, Li SM. (1996). Neural networks in control systems Mathematical perspectives on neural networks.

Niranjan M, Kadirkamanathan V. (1993). A function estimation Neural Comput. 5

Pintelon R, Rolain Y, Schoukens J, Nemeth JG, Crama P. (2003). Fast approximate identification of nonlinear systems Automatica. 39

Rojo-alvarez JL, Martinez-ramon M, De M, Artes-rodriguez A, Figueiras-vidal AR. (2004). Support vector method for robust ARMA system identification IEEE Trans Signal Process. 52

Scholkopf B, Smola AJ. (1998). On a kernel-based method for pattern recognition, regression, function approximation and operator inversion Algorithmica. 22

Tekalp AM. (1995). Digital video processing.

Tekalp AM, Kaufman H, Woods JW. (1985). Fast recursive estimation of the parameters of a space-varying autoregressive image model IEEE Trans Acoustics, Speech, and Signal Processing. 33

Ting-Ho Lo J. (1994). Synthetic approach to optimal filtering. IEEE transactions on neural networks. 5 [PubMed]

Vandewalle J, Suykens JAK, Brabanter J, Lukas L. (2002). Weighted least squares support vector machines: Robustness and sparse approximation Neurocomputing. 48

Vandewalle J, Suykens JAK, van_Gestel T, de_Brabanter J, de_Moor B. (2003). Least squares support vector machines.

Vapnik V et al. (1997). Predicting time series with support vector machines: Proceedings of ICANN.

Varshney PK. (1997). Multisensor data fusion J Electronics And Communication Engineering. 9

Xia Y. (1996). A new neural network for solving linear and quadratic programming problems. IEEE transactions on neural networks. 7 [PubMed]

Xia Y, Leung H, Bosse E. (2002). Neural data fusion algorithms based on a linearly constrained least square method. IEEE transactions on neural networks. 13 [PubMed]

Xia YS, Leung H. (2006). Spatial temporal prediction based on optimal fusion IEEE Trans Neural Networks. 17

Zheng WX. (1999). A least-squares based method for autoregressive signals in the presence of noise IEEE Trans Circuits And Systems II analog And Digital signal Processing. 46