Measuring ordinates of Volterra kernels of a dynamic nonlinear object using binary m-sequence as input with respect to the difference of test signal autocorrelation function from delta function

The paper considers algorithm for computing the ordinates of a Volterra kernels of a dynamic nonlinear object using binary M-sequence as input with respect to the difference of test signal autocorrelation function from ideal delta function. Computer simulation of identification of nonlinear dynamic objects was used to verify algorithms obtained.