document: Neural and neuro-fuzzy integration in a knowledge-based system for air quality prediction 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 , a 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
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