Adaptive training schema in Mamdani-type neuro-fuzzy models for data-analysis in dynamic system forecasting

Autor:	Wi-Meng Tan, Hiok-Chai Quek
Rok vydání:	2008
Předmět:	Neuro-fuzzy business.industry Computer science Fuzzy set Fuzzy control system Machine learning computer.software_genre Knowledge-based systems Robustness (computer science) Schema (psychology) Artificial intelligence Time series business computer Interpretability
Zdroj:	IJCNN
DOI:	10.1109/ijcnn.2008.4634032
Popis:	This paper investigates the possibility of a pseudo-online adaptive training schema for Mamdani-type neuro-fuzzy models that have robust linguistic interpretability. As such verbatim models are incapable of complex constructs available to Takagi-Sugeno-type neuro-fuzzy models, a heuristic approach is developed to allow the rule bases to adapt accordingly to fundamental shifts in the characteristics of time-varying dynamic systems for the purpose of forecasting. Experimental results showed that the proposed model is capable of adapting its rule base over time, and uses a relatively fewer number of rules for generalization in dynamic systems.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5f707279d9e4aeec08132de9299a1822 https://doi.org/10.1109/ijcnn.2008.4634032 Zobrazit plný text záznamu