Data driven modeling based on dynamic parsimonious fuzzy neural network

• Main Authors: Pratama, Mahardhika, ER, Meng Joo, LI, Xiang, OENTARYO, Richard Jayadi, Lughofer, Edwin, Arifin Imam
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Dynamic parsimonious fuzzy neural network (DPFNN); Radial basis function (RBF); Self organizing map (SOM); Rule growing; Rule pruning; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/3249
• Institution: Singapore Management University

In this paper, a novel fuzzy neural network termed as dynamic parsimonious fuzzy neural network (DPFNN) is proposed. DPFNN is a four layers network, which features coalescence between TSK (Takagi-Sugeno-Kang) fuzzy architecture and multivariate Gaussian kernels as membership functions. The training...