Nonlinear systems (vol. 2): applications to bilinear control
Nonlinear systems (vol. 2): applications to bilinear control
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Third Order Volterra System Identification
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Application of higher order spectral analysis to cubicallynonlinear system identification
IEEE Transactions on Signal Processing
Second-order Volterra system identification
IEEE Transactions on Signal Processing
Invertibility of higher order moment matrices
IEEE Transactions on Signal Processing
Efficient algorithms for Volterra system identification
IEEE Transactions on Signal Processing
Random and pseudorandom inputs for Volterra filter identification
IEEE Transactions on Signal Processing
Nonlinear system identification using Gaussian inputs
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Hi-index | 22.14 |
This paper is concerned with the identification of discrete-time, time invariant, state affine state space models driven by an independent identically distributed (IID) random input, and in the presence of process and measurement noise. The identification problem is treated using a cumulant based approach. It is shown that the input-output and input-state crosscumulant equations in the time domain have the form of a linear autonomous system. An algorithmic procedure is then developed, for the computation of the unknown system matrices, based on a standard deterministic linear subspace identification algorithm, provided the input signal has some persistent excitation properties. The special case of Gaussian IID input is also examined. The proposed method is computationally very efficient and its accuracy is illustrated by simulations.