Block-structured recurrent neural networks
Neural Networks
Simplification of fuzzy-neural systems using similarity analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
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In this paper, a recurrent compensatory neuro-fuzzy system (RCNFS) is proposed for prediction of time sequence. The compensatorybased 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.