Dynamic fuzzy neural networks-a novel approach to function approximation

By Shiqian Wu, Meng Joo Er. IEEE Trans. Syst. Man Cybern. Part B, 2000

468 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e003888611276e7fee5940124245b411c61a68dc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Control and Systems Engineering

Citations in the corpus

A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning (2008, unknown): “The performance of SEIT2FNN is compared with some recently developed type-1 fuzzy systems designed by genetic algorithms or neural learning, including the radial basis function- based adaptive fuzzy system (RBF-AFS) [40], hybrid neural fuzzy inference system (HyFIS) [41], neurofuzzy function approxi”
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models (2008, methodology): “Furthermore, it also contains a comparison with the evolving fuzzy modeling approaches eTS [9], SOFNN [13], GDFNN [17], SAFIS [14], DENFIS [11], MRAN [46], and RANEKF [47], based on two nonlinear dynamic identification systems and a three-input nonlinear function approximation task.”
Face recognition with radial basis function (RBF) neural networks (2002, methodology): “However, if the widths are too large, the interaction of different classes will be great and the output belonging to the class will not be so significant [25], while the output of other classes may be large so that it will lead to misclassification greatly.”
Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction (2006, methodology): “Using the ideas of adding and pruning hidden neurons to form a minimal RBF network in [20], a hierarchical on-line self-organizing learning algorithm for dynamic fuzzy neural networks (DFNN) has been proposed in [18].”
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks (2001, methodology): “Different from the aforementioned FNNs, several adaptive paradigms have been presented whereby not only can the connection weights be adjusted but also the structure can be self-adaptive during learning [16]–[18].”
NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches (2005, methodology): “In addition, performances of the proposed methodology are compared with some recently developed methods, including FNN methods such as adaptive network-based fuzzy inference system (ANFIS) [8], RBF-based adaptive fuzzy system (RBF-AFS) [4], fuzzy neural network system using similarity analysis (FNNS”
High-speed face recognition based on discrete cosine transform and RBF neural networks (2005, background): “Consequently, the nonuniform illumination effect can be reduced by discarding several low-frequency DCT coefficients.”
SOFMLS: Online Self-Organizing Fuzzy Modified Least-Squares Network (2009, background): “The system identiﬁcation can be classiﬁed into two groups: 1) ofﬂine identiﬁcation [4], [9], [18], [19], [30], [39], [42] and 2) online identiﬁcation [2], [3], [20], [21], [23], [24], [27], [28], [40], [41], [43].”