{'fraction_non_alphanumeric': 0.025, 'fraction_numerical': 0.0, 'mean_word_length': 5.341584158415841, '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}}
Intelligent Control of Active Suspension Systems

A self-organizing fuzzy controller (SOFC) has been proposed to control engineering applications. During the control process, the SOFC continually updates the learning strategy in the form of fuzzy rules, beginning with empty fuzzy rules. This eliminates the problem of finding appropriate membership functions and fuzzy rules for the design of a fuzzy logic controller. It is, however, arduous to select appropriate parameters (learning rate and weighting distribution) in the SOFC for control engineering applications. To solve the problem caused by the SOFC, this study developed a hybrid self-organizing fuzzy and radial basis-function neural-network controller (HSFRBNC). The HSFRBNC uses a radial basis-function neural-network (RBFN) to regulate in real time these parameters of the SOFC, so as to gain optimal values, thereby overcoming the problem of the SOFC application. To confirm the applicability of the proposed HSFRBNC, the HSFRBNC was applied in manipulating an active suspension system. Then, its control performance was evaluated. Simulation results demonstrated that the HSFRBNC offers better control performance than the SOFC in improving the service life of the suspension system and the ride comfort of a car.