Air Quality Prediction Using Neuro-Fuzzy Tools

Autor:	Severin Bumbaru, Elias Kalapanidas, Ciprian-Daniel Neagu, Nikolaos Avouris
Rok vydání:	2001
Předmět:	Structure (mathematical logic) Fuzzy rule Neuro-fuzzy Computer science business.industry Machine learning computer.software_genre Fuzzy logic Expert system Set (abstract data type) Connectionism Artificial intelligence Explicit knowledge business computer
Zdroj:	IFAC Proceedings Volumes. 34:229-235
ISSN:	1474-6670
DOI:	10.1016/s1474-6670(17)40822-6
Popis:	A unified approach for integrating explicit and implicit knowledge in connectionist expert systems is proposed for air quality prediction. The explicit knowledge is represented by discrete fuzzy rules, which are directly mapped into an equivalent neural structure. Learning data set is incorporated in a neuro-fuzzy module, representing implicit knowledge. The combination of explicit and implicit knowledge modules is viewed as a fuzzy rule-based model of the problem and is implemented by a supervised trained gating network. Results are encouraging and show that the method is worthy of further research.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e6ab17bbccb05dd4f2466b77348e9d45 https://doi.org/10.1016/s1474-6670(17)40822-6 Zobrazit plný text záznamu