By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ...
Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensation-based recurrent fuzzy neural network (CRFNN) by adding ...
This work proposes a novel compensation-based recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the ...
By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.
A novel compensation-based recurrent fuzzy neural network and its learning algorithm. Authors : Bo WU, Ke WU, JianHong L. DOI : https://doi.org/10.1007/s11432 ...
The CRFNN model is proven to be a universal approximator in this paper. Moreover, an online learning algorithm is proposed to automatically construct the CRFNN.
Missing: novel | Show results with:novel
A novel compensatory-based recurrent fuzzy neural network (CRFNN) is proposed by adding recurrent element and compensatory element to the conventional fuzzy ...
It consists of structure learning and parameter learning. The structure learning algorithm determines whether to add a new node to satisfy the fuzzy partition ...
Missing: novel | Show results with:novel
This paper presents a design methodology for predictive control of industrial processes via recurrent fuzzy neural networks (RFNNs).
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This book presents a systematic framework targeting at fuzzy modeling and fuzzy control of nonlinear systems with uncertainties. The book is organized into ...