Neural-Network-Based Fuzzy Logic Control and Decision System

By Chin-Teng Lin, C.S.G. Lee. 1991

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Citations in the corpus

The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms (2003, methodology): “Keywords: Genetic algorithms; Decision rules; Bankruptcy prediction”
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading (2017, background): “In fuzzy neural networks, the fuzzy representation part is conventionally connected to the input vector f t (green nodes) with different membership functions [35].”
A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms (2002, methodology): “for , according to the first-nearest-neighbor heuristic [11], where decides the overlap degree between two clusters.”
Binary Rule Generation via Hamming Clustering (2002, unknown): ““neuro-fuzzy” hybrid models [14], [15], [16], [17], which”
A Mixed-Integer Programming Approach to Multi-Class Data Classification Problem by (2005, methodology): “A general neural-network model for fuzzy logic control and decision systems including the data classification problem is discussed in [14].”
A literature review on the state-of-the-art in patent analysis (2014, methodology): “The associated parameters to infer about the appropriate technology are automatically constructed using Kohonen learning algorithm [45] and first nearest neighbor heuristic [46].”
Deep learning in robotics: a review of recent research (2017, background): “that was needed for operating dynamical systems in continuous spaces [13,14], and closely related fuzzy systems seemed well suited for nominal logical control decisions [15].”
Identification and control of dynamic systems using recurrent fuzzy neural networks (2000, background): “Subsequently, Chen and Teng and Ku and Lee, proposed control architecture for the model reference adaptive control (MRAC) problem by using FNNs [4], [5], and diagonal neural networks [8].”
A fuzzy ontology and its application to news summarization (2005, methodology): “In our previous work [8], we have presented a five-layer Fuzzy Inference Agent (FIA) [7], [10] for news event ontology extraction.”
An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network (2001, background): “Lin and Lee [28] also presented a FNN, capable of handling both the fuzzy inputs and outputs.”
Forecasting stock market short-term trends using a neuro-fuzzy based methodology (2009, unknown): “…2005 ; Doesken , Abraham , Thomas , & Paprzycki , 2005 ; Kosaka , Mizuno , Sasaki , Someya , & Hamada , 1991 ; Kuo , 1998 ; Kuo & Xue , 1998 ; Lin , Khan , & Huang , 2002 ; Lin & Lee , 1991 ; Nayak , Sudheer , Rangan , & Ramasastri , 2004 ; Siekmann , Gebhardt , & Kruse , 1999 ; Wikowska , 1995 ) .”
Ten years of genetic fuzzy systems: current framework and new trends (2001, background): “Fuzzy neural networks contribute to overcome lacking of linguistic representation and transparency, but the designer must decide on major design parameters such as universes granulation, rule antecedent aggregation operators, rule semantics, rule base aggregation operators and defuzzi5cation methods”
Symbolic Interpretation of Artificial Neural Networks (1999, methodology): “In this setting the neural network subsystem is typically used to adapt membership functions of fuzzy variables [5], or to refine and extract fuzzy rules [42], [41], [18].”
State-of-the-art in artificial neural network applications: A survey (2018, methodology): “The comparison was made based on (i) author(s)/year of publication (ii) ANN modeling (iii) ANN area of application (iii) studied contribution to human challenges (with references to Supplementary Table 2) [11, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, ”