Journal
INFORMATION SCIENCES
Volume 608, Issue -, Pages 313-338Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.06.071
Keywords
Nonstationary fuzzy neural network; Fuzzy c-means network clustering; Conjugate gradient method; Armijo-type search; Convergence
Categories
Funding
- National Key Research and Development Program of China [2018AAA0100100]
- National Natural Science Foundation of China [62173345]
- Major Scientific and Technological Projects of China National Petroleum Corporation (CNPC) [ZD2019-183-008]
- Fundamental Research Funds for the Central Universities [20CX05002A, 20CX05012A]
- Joint fund of Science and Technology Department of Liaoning Province [2021030195-JH3/103, 2021-KF-22-07]
- State Key Laboratory of Robotics
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This paper presents a nonstationary fuzzy neural network (NFNN) model that combines NFISs and NNs, achieving self-learning and improved translatability of NNs by synthesizing logical inference and language expression abilities with learning mechanisms.
Nonstationary fuzzy inference systems (NFISs) model the variation in opinions of individ-ual experts and expert groups. They have the capability similar to type-2 fuzzy systems in some regards with less computational costs. This paper presents a nonstationary fuzzy neural network (NFNN) model by combining NFISs and neural networks (NNs). The pro-posed model synthesizes the logical inference and language expression abilities of NFIS with the learning mechanism of NNs. Hence, it makes NNs more translatable and also achieves self-learning of fuzzy rules. Besides, a fuzzy c-means network (FCMnet) clustering and a modified conjugate gradient method with constrained Armijo-type rule (MCGA) are proposed to initialize and train NFNN, respectively. The proposed FCMnet first combines the fuzzy c-means (FCM) algorithm with NNs. Thus, it not only improves the performance of FCM, but also makes NNs more interpretable. For MCGA, we construct a specific conju-gate coefficient to ensure the sufficient descent property and propose a constrained Armijo-type rule to search a suitable learning rate in each iteration. By adopting these two techniques, MCGA achieves a fast convergence speed. In addition, both weak and strong convergence results are proven rigorously. Experiments on 14 datasets are carried out to illustrate the competitive performance of our models.(c) 2022 Published by Elsevier Inc.
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