Prediction of time sequence using recurrent compensatory neuro-fuzzy systems

Abstract

In this paper, a recurrent compensatory neuro-fuzzy system (RCNFS) is proposed for prediction of time sequence. The compensatory-based fuzzy reasoning method is using adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic systems more adaptive and effective. The recurrent network is embedded in the RCNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. Also, an on-line learning algorithm is proposed to automatically construct the RCNFS. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. © Springer-Verlag Berlin Heidelberg 2005.