{'fraction_non_alphanumeric': 0.01443001443001443, 'fraction_numerical': 0.0, 'mean_word_length': 5.609523809523809, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Instrumental variables based methods for linear systems identification with fractional models in the EIV context

In this paper a new instrumental variables methods based on the Higher-Order-Statistics (fourth order cumulants) are developed for continuous-time system identification with fractional models in the errors in variables context. The fractional orders are supposed known a priori and only the linear coefficients are estimated. The developed algorithms are compared to a fractional fourth order cumulants based least squares algorithm. Their performances are tested through a numerical example in two cases: white and colored noises affecting the input and the output measurements.