期刊论文

Li, Long

英文

Neural Processing Letters

1370-4621

2020

51

2

1093-1109

10.1007/s11063-019-10135-4

This project is partially supported by the Natural Science Foundation of China (11401185), the Hunan Provincial Natural Science Foundation of China (2017JJ2011, 14JJ6039), the Scientific Research Fund of Hunan Provincial Education Department (17A031, 13B004), the Science and Technology Plan Project of Hunan Province (Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, 2016TP1020) and China Scholarship Council. This project is partially supported by the Natural Science Foundation of China (11401185), the Hunan Provincial Natural Science Foundation of China (2017JJ2011, 14JJ6039), the Scientific Research Fund of Hunan Provincial Education Department (17A031, 13B004), the Science and Technology Plan Project of Hunan Province (Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, 2016TP1020) and China Scholarship Council.

本校为第一且通讯机构

In this paper, a smoothing algorithm with constant learning rate is presented for training two kinds of fuzzy neural networks (FNNs): max-product and max-min FNNs. Some weak and strong convergence results for the algorithm are provided with the error function monotonically decreasing, its gradient going to zero, and weight sequence tending to a fixed value during the iteration. Furthermore, conditions for the constant learning rate are specified to guarantee the convergence. Finally, three numerical examples are given to illustrate the feasibil...