Affiliations: [a] Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan
| [b] Microsoft Taiwan Corporation, Taipei, Taiwan
Correspondence:
[*]
Corresponding author. R.J. Kuo, Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei, Taiwan 106. Tel.: +886 227376328; Fax: +886 227376344; E-mail: rjkuo@mail.ntust.edu.tw.
Abstract: In this study, an intuitionistic fuzzy neural network (IFNN) with Gaussian membership function and Yager-generating function is proposed. Since intuitionistic fuzzy logic (IFL) considers membership, non-membership and hesitation values simultaneously, the incorporation of the concept of IFL into a fuzzy neural network (FNN) can enhance the performance of an FNN. A back-propagation learning algorithm is developed to optimize the IFNN parameters and weights. The proposed IFNN is applied to ten problems, including nonlinear control and prediction problems. The computational results indicate that the proposed IFNN is more efficient than conventional algorithms, such as artificial neural networks (ANN), fuzzy neural networks (FNN), and a support vector regression (SVR).
Keywords: Fuzzy neural network, intuitionistic fuzzy sets, fuzzy systems
