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

Ciprian Daniel Neagu, Nikolaos Avouris, Elias Kalapanidas, Vasile Palade

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)141-169
Number of pages29
JournalApplied Intelligence
Issue number2
Publication statusPublished - Sept 2002
Externally publishedYes


  • Air quality prediction
  • Fuzzy rule-based system
  • Modular structure
  • Neural and neuro-fuzzy integration

ASJC Scopus subject areas

  • Artificial Intelligence


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