A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks

By Shiqian Wu, Meng Joo Er, Yang Gao. IEEE transactions on fuzzy systems, 2001

415 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f2a2451a842cb632cabaf7bc5324ef1f946a2196

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

1. Edwin David Lughofer
2. J. J. Rubio

In Control and Systems Engineering

1. Edwin David Lughofer
2. J. J. Rubio

Citations in the corpus

• SOFMLS: Online Self-Organizing Fuzzy Modified Least-Squares Network (2009, background): “The proposed general DFNN (GDFNN) [41] tries to give reasonable explanations for some predefined training parameters in DFNN.”
• 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.”