Title :
A hierarchical recurrent neuro-fuzzy system
Author :
Nürnberger, Andreas
Author_Institution :
Fac. of Comput. Sci., Magdeburg Univ., Germany
Abstract :
Fuzzy systems, neural networks and their combination in neuro-fuzzy systems are already well established in data analysis and system control. Especially, neuro-fuzzy systems are well suited for the development of interactive data analysis tools, which enable the creation of rule-based knowledge from data and the introduction of a-priori knowledge into the process of data analysis. However, its recurrent variants-especially recurrent neuro-fuzzy models-are still rarely used. In this article a (hybrid) recurrent neuro-fuzzy model is presented which is designed for application in time series prediction and identification of dynamic systems. It has been implemented in a tool for the interactive design of hierarchical recurrent fuzzy systems
Keywords :
feedback; fuzzy neural nets; fuzzy systems; knowledge based systems; recurrent neural nets; time series; a-priori knowledge; data analysis; hierarchical recurrent neuro-fuzzy system; interactive data analysis tools; rule-based knowledge; system control; time series prediction; Computer science; Data analysis; Feedforward systems; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Gaussian processes; Learning systems; Logistics; Neural networks;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.943755
