Neuro-fuzzy rule generation: survey in soft computing framework

By S. Mitra, Yoichi Hayashi. IEEE Trans. Neural Networks Learn. Syst., 2000

784 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/04ab811930a86ffef0968c8d8555d833364a9af0

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Citations in the corpus

Fuzzy human motion analysis: A review (2014, background): “Mitra and Hayashi [195] provided an exhaustive survey on the neurofuzzy rule generation algorithms, while Cordón et al.”
Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction (2006, methodology): “The second task of parameter adjustment involves realizing the parameters for the fuzzy system structure determined in the previous step [ 15 ].”
Fuzzy decision making: A bibliometric-based review (2017, neutral): “Some authors have made compilations of meth-ods and applications for fuzzy decision-making [8, 10–13 , 19– Themainaimistopresentanoverallviewof theresearchintheﬁeldoffuzzydecision-making, fromtheworkpresentedbyBellmanandZadehin 1970[3],makinguseofbibliometrictechniques.”
Designing fuzzy inference systems from data: An interpretability-oriented review (2001, background): “In [38], [39], the authors build a network for classiﬁcation purposes.”
Rule Extraction from Neural Network by Genetic Algorithm with Pareto Optimization (2004, methodology): “This is the reason to develop methods extracting knowledge from NN in the comprehensible way [3], [2], [4].”
Neuro-fuzzy control of a robotic exoskeleton with EMG signals (2004, background): “If the patterns of EMG signals for certain motion are completely unknown, control rules extraction is not required, and preparation time is not limited, a well structured adaptive neuro controller [13] or neuro-fuzzy controller [14] might be one of the most suitable controllers for the control of…”
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems (2006, background): “An excellent survey is given in [212] for neuro-fuzzy rule generation in a more gen-”
Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification (2020, background): “The main advantage is that they are universal approximators and capable of adapting interpretable IF-THEN rules to training data [2].”
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, neutral): “Neuro-fuzzy hybridization is widely termed as fuzzy neural networks (FNNs) or neuro-fuzzy systems (NFSs) in the literature [27].”
A survey on industrial applications of fuzzy control (2011, neutral): “A very good survey on neuro-fuzzy rule generation in a rather general setting of soft computation is given in [26].”