Identification of cubically nonlinear systems using undersampled data

A practical technique for identification of cubically nonlinear systems using higher order spectra of the discrete data samples of the system input and output is proposed. This technique differs from the conventional one in that it only requires the sampling frequency for the system output to be equal to twice the bandwidth of the system input, instead of six times the bandwidth of the system input. This means the demand for high speed processing and a large amount of data in the conventional approach can be greatly relieved, Two methods are developed: one is suitable for systems with a Gaussian random input, the other is suitable for systems with a non-Gaussian random input. The advantages of the two methods over their conventional counterparts are demonstrated via computer simulation