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Intelligent air quality detection based on genetic algorithm and neural network: An urban China case study
Author(s) -
Zhang Bi,
Li Wei
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4673
Subject(s) - artificial neural network , computer science , genetic algorithm , fuzzy logic , air quality index , task (project management) , selection (genetic algorithm) , artificial intelligence , quality (philosophy) , neuro fuzzy , machine learning , data mining , fuzzy control system , engineering , systems engineering , geography , philosophy , epistemology , meteorology
Summary Scientific and objective evaluations of atmospheric quality have become a primary task for researchers with the continuous development of modern industrial processes. At present, various approaches are used in monitoring air quality. The core factors of these approaches are the selection and establishment of an intelligent evaluation model. In this study, we designed a fuzzy genetic neural network model that fuses data based on the characteristics of autonomic learning and self‐organization and optimizes the fuzzy system by using the neural network model. A simulation was conducted to verify the feasibility of the algorithm. Results indicate that the proposed algorithm is not only a highly objective, scientific, and accurate method for detecting atmospheric environmental quality but also a practical solution.