A hierarchical recurrent neuro-fuzzy system

Fuzzy systems, neural networks and its 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.