{'fraction_non_alphanumeric': 0.01990632318501171, 'fraction_numerical': 0.0, 'mean_word_length': 4.979020979020979, '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}}
Improvement of NeuroFuzzy Systems Using K-means Clustering

A NeuroFuzzy System (NFS) is one of the most commonly used systems in the real life problems, it has been recognized as a powerful tool which can facilitate the effective development of models by combining information from different sources, such as empirical models, heuristics and data. Neuro-fuzzy models describe systems by means of fuzzy if-then rules represented in a network structure to which learning algorithms known from the area of artificial neural networks can be applied. This paper examines a K-means clustering algorithm to enhance NFS efficient. The enhancement of NFS by using K-means will be shown by running two of experiments on nonlinear functions. The obtained results will show that, the efficiency of NFS with K-means clustering gives better results than original NFS.