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Genetic Algorithm Based Hybrid Fuzzy System for Assessing Morningness
Author(s) -
Animesh Biswas,
D. Dutta Majumder
Publication year - 2014
Publication title -
advances in fuzzy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 19
eISSN - 1687-711X
pISSN - 1687-7101
DOI - 10.1155/2014/732831
Subject(s) - fuzzy logic , fuzzy inference system , computer science , adaptive neuro fuzzy inference system , inference , process (computing) , measure (data warehouse) , psychology , machine learning , genetic algorithm , artificial intelligence , fuzzy control system , cognitive psychology , data mining , operating system
This paper describes a real life case example on the assessment process of morningness of individuals using genetic algorithm based hybrid fuzzy system. It is observed that physical and mental performance of human beings in different time slots of a day are majorly influenced by morningness orientation of those individuals. To measure the morningness of people various self-reported questionnaires were developed by different researchers in the past. Among them reduced version of Morningness-Eveningness Questionnaire is mostly accepted. Almost all of the linguistic terms used in questionnaires are fuzzily defined. So, assessing them in crisp environments with their responses does not seem to be justifiable. Fuzzy approach based research works for assessing morningness of people are very few in the literature. In this paper, genetic algorithm is used to tune the parameters of a Mamdani fuzzy inference model to minimize error with their predicted outputs for assessing morningness of people

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