@inproceedings{4e9c2fe6393e45a78de203c78cfee89a,
title = "Infinite Fuzzy Restricted Boltzmann Machines for the Enhancement of Fuzzy Neural Networks",
abstract = "Nowadays, fuzzy restricted Boltzmann machines (FRBMs) enjoy wide attention in deep learning and fuzzy systems. As a fuzzy neural network, FRBM is the extension of the classic RBM. However, FRBM has inherent defects because it relies heavily on the size of hidden units for feature extraction. In this paper, a novel infinite FRBM (IFRBM) is proposed by extending the hidden units from fixed parameters to infinite ones. The structure of the FRBM is further improved guaranteed by a novel infinite energy function without specifying the hidden elements. Specifically, the proposed model can increase generated hidden units adaptively during training when encountering different training data. Comparative experiments demonstrate that our IFRBM can obtain competitive performance than the state-of-the-art model in terms of feature extraction and image classification. Besides, the proposed model tends to more concise network structure while not requiring the tuning of hidden hyper-parameter or additional expert experience.",
keywords = "deep learning, fuzzy systems, IFRBM, neural networks, restricted Boltzmann machines",
author = "Qianglong Wang and Xiaoguang Gao and Zijian Hu and Kaifang Wan",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 7th International Conference on Control and Robotics Engineering, ICCRE 2022 ; Conference date: 15-04-2022 Through 17-04-2022",
year = "2022",
doi = "10.1109/ICCRE55123.2022.9770246",
language = "英语",
series = "2022 7th International Conference on Control and Robotics Engineering, ICCRE 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "133--138",
booktitle = "2022 7th International Conference on Control and Robotics Engineering, ICCRE 2022",
}