{'fraction_non_alphanumeric': 0.017592592592592594, 'fraction_numerical': 0.0, 'mean_word_length': 5.591463414634147, '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}}
Recursive possibilistic fuzzy modeling

This paper suggests a recursive possibilistic approach for fuzzy modeling of time-varying processes. The approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling. Recursive possibilistic fuzzy modeling (rPFM) employs memberships and typicalities to cluster data. Functional fuzzy models uses affine functions in the fuzzy rule consequents. The parameters of the consequent functions are computed using the recursive least squares. Two classic benchmarks, Mackey-Glass time series and Box & Jenkins furnace data, are studied to illustrate the rPFM modeling and applicability. Data produced by a synthetic model with parameter drift is used to show the usefulness of rPFM to model time-varying processes. Performance of rPFM is compared with well established recursive fuzzy and neural fuzzy modeling and identification. The results show that recursive possibilistic fuzzy modeling produces parsimonious models with comparable or better accuracy than the alternative approaches.