Identification of non-minimum phase linear stochastic systems
Automatica (Journal of IFAC)
Time series: theory and methods
Time series: theory and methods
Identification of linear stochastic systems via second- and fourth-order cumulant matching
IEEE Transactions on Information Theory
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Adaptive identification of nonminimum phase ARMA models usinghigher order cumulants alone
IEEE Transactions on Signal Processing
FIR system identification using higher order cumulants-ageneralized approach
IEEE Transactions on Signal Processing
MA parameter estimation and cumulant enhancement
IEEE Transactions on Signal Processing
Identification of nonminimum phase FIR: systems using the third and fourth-order cumulants
IEEE Transactions on Signal Processing
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Two approaches are introduced for the identification of linear time-invariant systems when only output data are available. The input sequences are independent and must be non-Gaussian. To estimate the parameters of the system, we use only the fourth-order cumulants of the output, which may be contaminated by an additive, zero mean, Gaussian noise of unknown variance. To measure the performance of the proposed algorithms against existing methods, we compared them with the Zhang's algorithm. Simulations verify an apparent performance of the second algorithm, compared with the first and Zhang's algorithms, in a low signal-to-noise ratio and for small data. The simulation results show that the first algorithm has the same performance compared with Zhang's one. But the second algorithm achieves better performance compared with the first and Zhang is algorithm. For validation purposes, the second algorithm is used to search for a model able to describe and simulate the data set representing the wind speed.