{'fraction_non_alphanumeric': 0.028423772609819122, 'fraction_numerical': 0.006029285099052541, 'mean_word_length': 4.958974358974359, '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}}
Meta-cognitive Interval Type-2 neuro-fuzzy inference system for wind prediction

In this paper, we propose an Interval Type-2 neuro-fuzzy inference system and its meta-cognitive projection based learning algorithm (PBL-McIT2FIS) for wind speed prediction. Interval Type-2 fuzzy sets are employed in the antecedent of fuzzy rules and the consequent realizes Takagi-Sugeno-Kang (TSK) inference mechanism. Initially the rule base in PBL-McIT2FIS is empty, the learning algorithm employs prediction error and novelty of sample as a measure to add rules to network. As each sample is presented to network, the meta-cognitive component decides on whether to delete the sample without learning, learn the sample by adding a new rule, update the existing rules or reserve the sample for future use. Whenever a new rule is added or parameters of existing rules are updated, a projection based learning algorithm is employed to compute the optimal weights of the network. Performance of PBL-McIT2FIS is evaluated on a real world wind prediction problem and compared with support vector regression and OS-fuzzy-ELM. The results indicate better performance of PBL-McIT2FIS.