[1]HUANG Xiaoke,LIU Haitao,WANG Peizhuang.The restricted Boltzmann machine fuses picture fuzzy information[J].CAAI Transactions on Intelligent Systems,2025,20(5):1103-1111.[doi:10.11992/tis.202412008]
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The restricted Boltzmann machine fuses picture fuzzy information

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 5 Page number: 1103-1111 Column: 学术论文—机器学习 Public date: 2025-09-05
Title:
The restricted Boltzmann machine fuses picture fuzzy information
Author(s):
HUANG Xiaoke1;  LIU Haitao1; 2;  WANG Peizhuang2
1. College of Science, Liaoning Technical University, Fuxin 12300, China;
2. Institute of Intelligence Engineering and Mathematics, Liaoning Technical University, Fuxin 123000, China
Keywords:
deep learning; picture fuzzy numbers; restricted Boltzmann machine; picture fuzzy free energy function; accuracy function; defuzzify; contrastive divergence; reconstruction error
CLC:
TP391.4
DOI:
10.11992/tis.202412008
Abstract:
To solve the problem of insufficient representation ability of the restricted Boltzmann machine (RBM), a novel RBM model incorporating picture fuzzy information is proposed. First, the exact value parameter that limits the learning ability of the classical RBM is extended by the picture fuzzy numbers, which allow a multidimensional representation of information. Second, combined with the idea of precision function, the picture fuzzy free energy function is defuzzified, and then a new optimization target and learning algorithm are constructed. Finally, based on the multi-perspective comparative analysis using multiple benchmark datasets, it is demonstrated that the new model can effectively improve the representation and generalization capabilities of the classical model as well as various extended versions.
References:
[1] 马世龙, 乌尼日其其格, 李小平. 大数据与深度学习综述[J]. 智能系统学报, 2016, 11(6): 728-742.
MA Shilong, WUNIRI Qiqige, LI Xiaoping. Deep learning with big data: state of the art and development[J]. CAAI transactions on intelligent systems, 2016, 11(6): 728-742.
[2] HINTON G E. On early inspiration[Z/OL]. (2024-10-09) [2024-10-20]. https://ting.eudic.net/webting/desktopplay?id=dc3354a2-85e1-11ef-8108005056866eda.
[3] 胡铭菲, 左信, 刘建伟. 深度生成模型综述[J]. 自动化学报, 2022, 48(1): 40-74.
HU Mingfei, ZUO Xin, LIU Jianwei. Survey on deep generative model[J]. Acta automatica sinica, 2022, 48(1): 40-74.
[4] ALBERICI D, CONTUCCI P, MINGIONE E. Deep Boltzmann machines: rigorous results at arbitrary depth[J]. Annales Henri poincaré, 2021, 22(8): 2619-2642.
[5] WANG Zhendong, ZENG Yong, LIU Yaodi, et al. Deep belief network integrating improved kernel-based extreme learning machine for network intrusion detection[J]. IEEE access, 2021, 9: 16062-16091.
[6] HAMMOUCHE R, ATTIA A, AKHROUF S, et al. Gabor filter bank with deep autoencoder based face recognition system[J]. Expert systems with applications, 2022, 197: 116743.
[7] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recognition at scale[C]//International Conference on Learning Representations. NewOrleans: ICLR, 2021: 1-22.
[8] CHEN Leiyu, LI Shaobo, BAI Qiang, et al. Review of image classification algorithms based on convolutional neural networks[J]. Remote sensing, 2021, 13(22): 4712.
[9] VRáBEL J, PO?íZKA P, KAISER J. Restricted Boltzmann machine method for dimensionality reduction of large spectroscopic data[J]. Spectrochimica acta part B: atomic spectroscopy, 2020, 167: 105849.
[10] TYAGI P K, AGRAWAL D. Automatic detection of sleep apnea from single-lead ECG signal using enhanced-deep belief network model[J]. Biomedical signal processing and control, 2023, 80: 104401.
[11] KOREN Y, RENDLE S, BELL R. Advances in collaborative filtering[M]//Recommender Systems Handbook. New York: Springer US, 2021: 91-142.
[12] 汪强龙, 高晓光, 吴必聪, 等. 受限玻尔兹曼机及其变体研究综述[J]. 系统工程与电子技术, 2024, 46(7): 2323-2345.
WANG Qianglong, GAO Xiaoguang, WU Bicong, et al. Review of research on restricted Boltzmann machine and its variants[J]. Systems engineering and electronics, 2024, 46(7): 2323-2345.
[13] GU Linyan, YANG Lihua, ZHOU Feng. Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks[J]. Neural networks, 2022, 153: 49-63.
[14] CHEN Zixiang, MA Wanqi, DAI Wei, et al. Conditional restricted Boltzmann machine for item recommendation[J]. Neurocomputing, 2020, 385: 269-277.
[15] 李晓慧, 汪西莉. 结合卷积受限玻尔兹曼机的CV图像分割模型[J]. 激光与光电子学进展, 2020, 57(4): 041018.
LI Xiaohui, WANG Xili. CV image segmentation model combining convolutional restricted Boltzmann machine[J]. Laser & optoelectronics progress, 2020, 57(4): 201-212.
[16] HINTON G E. Training products of experts by minimizing contrastive divergence[J]. Neural computation, 2002, 14(8): 1771-1800.
[17] LUO Weijian, JIANG Hao, HU Tianyang, et al. Training energy-based models with diffusion contrastive divergences[EB/OL]. (2023-07-04)[2024-10-20]. https://arxiv.org/abs/2307.01668.
[18] TERENIN A, SIMPSON D, DRAPER D. Asynchronous gibbs sampling[C]//International Conference on Artificial Intelligence and Statistics. [S. l. ]: PMLR, 2020: 144-154.
[19] CHEN C L P, ZHANG Chunyang, CHEN Long, et al. Fuzzy restricted Boltzmann machine for the enhancement of deep learning[J]. IEEE transactions on fuzzy systems, 2015, 23(6): 2163-2173.
[20] JANMAIJAYA M, SHUKLA A K, SETH T, et al. Interval type-2 fuzzy restricted Boltzmann machine for the enhancement of deep learning[C]//2019 IEEE International Conference on Fuzzy Systems. New Orleans: IEEE, 2019: 1-6.
[21] FENG Shuang, CHEN C L P. A fuzzy restricted Boltzmann machine: novel learning algorithms based on the crisp possibilistic mean value of fuzzy numbers[J]. IEEE transactions on fuzzy systems, 2018, 26(1): 117-130.
[22] 张健, 丁世飞, 张楠, 等. 受限玻尔兹曼机研究综述[J]. 软件学报, 2019, 30(7): 2073-2090.
ZHANG Jian, DING Shifei, ZHANG Nan, et al. Restricted Boltzmann machines: a review[J]. Journal of software, 2019, 30(7): 2073-2090.
[23] CUONG B C, KREINOVICH V. Picture fuzzy sets-a new concept for computational intelligence problems[C]//2013 Third World Congress on Information and Communication Technologies. Hanoi: IEEE, 2013: 1-6.
[24] ZHU Sijia, LIU Zhe. Distance measures of picture fuzzy sets and interval-valued picture fuzzy sets with their applications[J]. AIMS mathematics, 2023, 8(12): 29817-29848.
[25] LYU Xueqin, MENG Lingzheng, CHEN Chao, et al. Fuzzy removing redundancy restricted Boltzmann machine: improving learning speed and classification accuracy[J]. IEEE transactions on fuzzy systems, 2020, 28(10): 2495-2509.
[26] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[27] XIAO Han, RASUL K, VOLLGRAF R. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms[EB/OL]. (2017-08-25)[2024-10-20]. https://arxiv.org/abs/1708.07747.
[28] COHEN G, AFSHAR S, TAPSON J, et al. EMNIST: Extending MNIST to handwritten letters[C]//2017 International Joint Conference on Neural Networks. Anchorage: IEEE, 2017: 2921-2926.
[29] CLANUWAT T, BOBER-IRIZAR M, KITAMOTO A, et al. Deep learning for classical Japanese literature[EB/OL]. (2018-12-03)[2024-10-20]. https://arxiv.org/abs/1812.01718.
[30] HSIEH P C, CHEN C P. Multi-task learning on MNIST image datasets[C]//International Conference on Learning Representations. Vancouver: OpenReview.net, 2018: 1-7.
[31] ACHARYA S, PANT A K, GYAWALI P K. Deep learning based large scale handwritten Devanagari character recognition[C]//2015 9th International Conference on Software, Knowledge, Information Management and Applications. Kathmandu: IEEE, 2015: 1-6.
[32] SOLTANZADEH H, RAHMATI M. Recognition of Persian handwritten digits using image profiles of multiple orientations[J]. Pattern recognition letters, 2004, 25(14): 1569-1576.
[33] KRIZHEVSKY A, HINTON G. Learning multiple layers of features from tiny images[EB/OL]. (2009-04-08)[2024-12-11]. https://www.cs.utoronto.ca/~kriz/learning-features-2009-TR.pdf.
[34] 张艳霞. 基于受限玻尔兹曼机的深度学习模型及其应用[D]. 成都: 电子科技大学, 2016.
ZHANG Yanxia. Deep learning models and applications based on the restricted Boltzmann machine[D]. Chengdu: University of Electronic Science and Technology, 2016.
[35] GLASER P, HUANG K H, GRETTON A. Near-optimality of contrastive divergence algorithms[C]//Proceedings of the 38th International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2024: 91036-91090.
[36] ZHENG Yujun, SHENG Weiguo, SUN Xingming, et al. Airline passenger profiling based on fuzzy deep machine learning[J]. IEEE transactions on neural networks and learning systems, 28(12): 2911-2923.
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