The identification of nonlinear biological systems: LNL cascade models
Biological Cybernetics
The fractal dimension of a test signal: implications for system identification procedures
Biological Cybernetics
Approximation of Myopic Systems Whose Inputs Need Not BeContinuous
Multidimensional Systems and Signal Processing
A bibliography on nonlinear system identification
Signal Processing - Special section on digital signal processing for multimedia communications and services
Diagonal Kernel point estimation of nth-order discrete Volterra-Wiener systems
EURASIP Journal on Applied Signal Processing
Parallel-cascade realizations and approximations of truncatedVolterra systems
IEEE Transactions on Signal Processing
Wavelet-based transformations for nonlinear signal processing
IEEE Transactions on Signal Processing
Efficient algorithms for Volterra system identification
IEEE Transactions on Signal Processing
Nonlinear parametric models from volterra kernels measurements
Mathematical and Computer Modelling: An International Journal
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This paper concerns the identification of nonlinear discrete causal systems that can be approximated with the Wiener--Volterra series. Some advances in the efficient use of Lee--Schetzen (L--S) method are presented, which make practical the estimate of long memory and high order models. Major problems in L--S method occur in the identification of diagonal kernel elements. Two approaches have been considered: approximation of gridded data, with interpolation or smoothing, and improved techniques for diagonal elements estimation. A comparison of diagonal elements estimated, with different methods has been shown with extended tests on fifth order Volterra systems.