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    Neural and Neuro-Fuzzy Integration in a Knowledge-Based System for Air Quality Prediction.

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    Publication date
    2002
    Author
    Neagu, Daniel
    Avouris, N.M.
    Kalapanidas, E.
    Palade, V.
    Keyword
    Air quality prediction
    Fuzzy rule-based system
    Modular structure
    Neural and neuro-fuzzy integration
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    In this paper we propose a unified approach for integrating implicit and explicit knowledge in neurosymbolic systems as a combination of neural and neuro-fuzzy modules. In the developed hybrid system, training data set is used for building neuro-fuzzy modules, and represents implicit domain knowledge. The explicit domain knowledge on the other hand is represented by fuzzy rules, which are directly mapped into equivalent neural structures. The aim of this approach is to improve the abilities of modular neural structures, which are based on incomplete learning data sets, since the knowledge acquired from human experts is taken into account for adapting the general neural architecture. Three methods to combine the explicit and implicit knowledge modules are proposed. The techniques used to extract fuzzy rules from neural implicit knowledge modules are described. These techniques improve the structure and the behavior of the entire system. The proposed methodology has been applied in the field of air quality prediction with very encouraging results. These experiments show that the method is worth further investigation.
    URI
    http://hdl.handle.net/10454/2970
    Version
    No full-text available in the repository
    Citation
    Neagu, C., Avouris, N.M., Kalapanidas, E. and Palade, V. (2002). Neural and Neuro-Fuzzy Integration in a Knowledge-Based System for Air Quality Prediction. Applied Intelligence. Vol. 17, No. 2, pp. 141-169.
    Link to publisher’s version
    http://www.springerlink.com/content/8rjeutvlxvvdjr1h/
    Type
    Article
    Collections
    Engineering and Informatics Publications

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