Journal ArticleDOI
Exact orthogonal kernel estimation from finite data records: extending Wiener's identification of nonlinear systems.
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Simulations are provided to demonstrate that the technique is more accurate than the Lee-Schetzen method with a white Gaussian input of limited duration, retaining its superiority when the system output is corrupted by noise.Abstract:Â
A technique is described for exact estimation of kernels in functional expansions for nonlinear systems. The technique operates by orthogonalizing over the data record and in so doing permits a wide variety of input excitation. In particular, the excitation is not limited to inputs that are white, Gaussian, or lengthy. Diagonal kernel values can be estimated, without modification, as accurately as off-diagonal values. Simulations are provided to demonstrate that the technique is more accurate than the Lee-Schetzen method with a white Gaussian input of limited duration, retaining its superiority when the system output is corrupted by noise.read more
Citations
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Journal ArticleDOI
A bibliography on nonlinear system identification
TL;DR: The present bibliography represents a comprehensive list of references on nonlinear system identification and its applications in signal processing, communications, and biomedical engineering.
Journal ArticleDOI
Identifying nonlinear difference equation and functional expansion representations : the fast orthogonal algorithm
TL;DR: A method is presented for identifying functional expansion and difference equation representations for nonlinear systems which greatly reduces computing time, so that 15-fold increases in speed of estimating kernels or difference equation coefficients are readily obtainable, compared with a previous orthogonal technique.
Journal ArticleDOI
Parallel cascade identification and kernel estimation for nonlinear systems.
TL;DR: It is shown that any discrete-time finite-memory nonlinear system having a finite-order Volterra series representation can be exactly represented by a finite number of parallel LN cascade paths.
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A robust orthogonal algorithm for system identification and time-series analysis
TL;DR: Methods for obtaining a parsimonious sinusoidal series representation or model of biological time-series data are described and illustrated, capable of higher resolution than a conventional Fourier series analysis and used to identify nonlinear systems with unknown structure.
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The identification of nonlinear biological systems: Volterra kernel approaches.
TL;DR: A recent kernel estimation technique that has proved to be effective in a number of biomedical applications is investigated as to running time and demonstrated on both clean and noisy data records, then it is used to illustrate identification of cascades of alternating dynamic linear and static nonlinear systems.
References
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Book
Nonlinear Problems in Random Theory
Norbert Wiener,T. Teichmann +1 more
TL;DR: A series of lectures on the role of nonlinear processes in physics, mathematics, electrical engineering, physiology, and communication theory was given in this article, where the last few of these were devoted to the application of my ideas to problems in the statistical mechanics of gases.
Journal ArticleDOI
The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
W I Hunter,Michael J. Korenberg +1 more
TL;DR: Various identification schemes that have been proposed for the Hammerstein and Wiener systems are critically reviewed with reference to the special problems that arise in the identification of nonlinear biological systems.
Journal ArticleDOI
Measurement of the Wiener Kernels of a Non-linear System by Cross-correlationâ€
Y. W. Lee,M. Schetzen +1 more
TL;DR: In this article, a practical and relatively simple method of measuring the Wiener kernels of a non-linear system is presented, which is based upon cross-correlation techniques and avoids orthogonal expansions such as those of the original Wiener method of measurement.
Journal ArticleDOI
The identification of nonlinear biological systems: LNL cascade models
TL;DR: A simulated LNL system is identified from limited duration input-output data using an iterative identification scheme and various identification schemes proposed are critically reviewed with reference to the special problems that arise in the identification of nonlinear biological systems.