Title :
Adaptive training schema in Mamdani-type neuro-fuzzy models for data-analysis in dynamic system forecasting
Author :
Tan, Wi-Meng ; Quek, Hiok-Chai
Author_Institution :
Centre for Comput. Intell., Nanyang Technol. Univ., Singapore
Abstract :
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.
Keywords :
data analysis; fuzzy neural nets; knowledge based systems; learning (artificial intelligence); time-varying systems; Mamdani-type neuro-fuzzy models; Takagi-Sugeno-type neuro-fuzzy models; data-analysis; dynamic system forecasting; pseudo-online adaptive training schema; robust linguistic interpretability; rule bases; time-varying dynamic systems; Chaos; Decision making; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Predictive models; Robustness; System testing; Takagi-Sugeno model; Time varying systems;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634032