{'fraction_non_alphanumeric': 0.02365930599369085, 'fraction_numerical': 0.0, 'mean_word_length': 4.87962962962963, '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}}
Adaptive fuzzy control

In this paper, adaptive fuzzy logic control (FLC) will be designed by using a simple reference model. This design approach is based on our new methodology "design rule base qualitatively and data base quantitatively". If the linear rule base is used, the model of FLC can be obtained. It is actually a nonlinear function with only three scaling gains need to be designed and tuned. The conventional control theory can thus be used. This model reference adaptive fuzzy control (MRAFC) requires less restriction on the reference model, but often achieves a more robust performance than its classical counterpart.