Books on the topic 'Linear and Nonlinear System identification'

Billings, S. A. Piecewise linear identification of nonlinear systems. Sheffield: University,Dept. of Control Engineering, 1986.

Santos, Paulo Lopes dos. Linear parameter-varying system identification: New developments and trends. Singapore: World Scientific, 2012.

Coca, D. Continuous-time system identification for linear and nonlinear systems using wavelet decomposition. Sheffield: University of Sheffield, Department of Automatic Control and Systems Engineering, 1996.

Tsang, K. M. Identification of multi-class linear and nonlinear systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.

Li, L. M. Continuous time linear and nonlinear system identification in the frequency domain. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

Prakriya, Shankar. Blind identification of linear and nonlinear systems with cycloststionary inputs. Ottawa: National Library of Canada, 1993.

Bendat, Julius S. Nonlinear system analysis and identification from random data. New York: Wiley, 1990.

United States. National Aeronautics and Space Administration., ed. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. [Washington, D.C: National Aeronautics and Space Administration, 1997.

Center, Langley Research, ed. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

Billings, Stephen A. Nonlinear System Identification. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118535561.

Nelles, Oliver. Nonlinear System Identification. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3.

Nelles, Oliver. Nonlinear System Identification. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3.

Ogunfunmi, Tokunbo. Adaptive Nonlinear System Identification. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-68630-1.

Giri, Fouad, and Er-Wei Bai, eds. Block-oriented Nonlinear System Identification. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-513-2.

Śliwiński, Przemysław. Nonlinear System Identification by Haar Wavelets. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29396-2.

Nalitolela, Noel Gerald. System identification of linear vibrating structures. Birmingham: Aston University. Department ofMechanical and Production Engineering, 1991.

Greblicki, Włodzimierz. Nonparametric system identification. Cambridge: Cambridge University Press, 2008.

Haber, Robert, and László Keviczky. Nonlinear System Identification — Input-Output Modeling Approach. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4481-0.

Haber, Robert. Nonlinear system identification: Input-output modeling approach. Dordrecht: Kluwer Academic Publishers, 1999.

Vidyasagar, M. Non-linear systems analysis. 2nd ed. London: Prentice-Hall International (UK), 1993.

Bendat, Julius S. Nonlinear system techniques and applications. New York: Wiley, 1998.

Greblicki, Włodzimierz. Non-parametric system identification. Cambridge: Cambridge University Press, 2008.

Zheng, G. L. Qualitative validation and generalization in nonlinear system identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

Juang, Jer-Nan. Linear system identification via backward-time observer models. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

Juang, Jer-Nan. Linear system identification via backward-time observer models. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

Non-linear time series: A dynamical system approach. Oxford: Clarendon, 1990.

Tong, Howell. Non-linear time series: A dynamical system approach. Oxford: Clarendon Press, 1990.

Tong, Howell. Non-linear time series: A dynamical system approach. Oxford: Clarendon Press, 1993.

Recursive identification based on the nonlinear Wiener model. Uppsala: Academia Upsaliensis, 1990.

Overschee, Peter van. Subspace identification for linear systems: Theory, implementation, applications. Boston: Kluwer Academic Publishers, 1996.

Vakakis, Alexander F., ed. Advanced Nonlinear Strategies for Vibration Mitigation and System Identification. Vienna: Springer Vienna, 2010. http://dx.doi.org/10.1007/978-3-7091-0205-3.

Tomlinson, G. R. Higher order frequency response functions in nonlinear system identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.

Mao, K. Z. Multi-directional model validity tests for nonlinear system identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1997.

Haynes, B. R. Global analysis and model validation in nonlinear system identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.

Vakakis, Alexander F. Advanced nonlinear strategies for vibration mitigation and system identification. Wien: Springer, 2010.

Jer-Nan, Juang, and Langley Research Center, eds. Identifiability of linear systems in physical coordinates. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

Jer-Nan, Juang, and Langley Research Center, eds. Identifiability of linear systems in physical coordinates. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

Kahn, Peter B. Mathematical methods for scientists and engineers: Linear and nonlinear system. New York: Wiley, 1990.

Phan, Minh Q. Identification of linear systems by an asymptotically stable observer. Hampton, Va: Langley Research Center, 1992.

Crespo, Luis G. Differential flatness and cooperative tracking in the Lorenz system. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2002.

M, Deistler, ed. The statistical theory of linear systems. New York: Wiley, 1988.

Zheng, G. L. Effects of overparametrisation in nonlinear system identification and neural networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

Hanzon, Bernard. Identifiability, recursive identification and spaces of linear dynamical systems. Amsterdam, the Netherlands: Stichting Mathematische Centrum, 1989.

Schetzen, Martin. The Volterra and Wiener theories of nonlinear systems. Malabar, Fla: R.E. Krieger Pub. Co., 1989.

Kaikko, Juha. Performance prediction of gas turbines by solving a system of non-linear equations. Lappeenranta, Finland: Lappeenranta University of Technology, 1998.

Chen, S. Neural networks for non-linear dynamic system modelling and identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.

Billings, S. A. The identification of linear and nonlinear models of a turbocharged atomotive diesel engine. Sheffield: University of Sheffield, Dept. of Control Engineering, 1988.

R, Pintelon, ed. Identification of linear systems: A pracatical guidline to accurate modeling. Oxford: Pergamon Press, 1991.

Overschee, Peter. Subspace Identification for Linear Systems: Theory - Implementation - Applications. Boston, MA: Springer US, 1996.

Hannan, E. J. The statistical theory of linear systems. Philadelphia: Society for Industrial and Applied Mathematics, 2012.