{'fraction_non_alphanumeric': 0.015170670037926675, 'fraction_numerical': 0.0, 'mean_word_length': 5.1875, '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 based on genetic algorithm for vertical electric furnace

The vertical electric furnace is a multi-variable complex system, conventional control methods are used to control it, to need modelling and decoupling. In this paper, a new approach that automates the optimization of fuzzy control by using improved genetic algorithm is proposed for the vertical electric furnace. This algorithm is an on-line technique and is used to tune the parameters of the fuzzy controller and to eliminate coupling between the upper and the lower temperature regions of the vertical electric furnace in real-time. In the industrial control network, the experiments of controlling the vertical electric furnace are performed. The results show that this method is efficient and practical.