具备视角协同学习能力的多视角TSK型模糊系统

程旸; 顾晓清; 蒋亦樟; 杭文龙; 钱鹏江; 王士同

doi:10.11999/JEIT151209

具备视角协同学习能力的多视角TSK型模糊系统

doi: 10.11999/JEIT151209

基金项目:

国家自然科学基金(61300151)，江苏省自然科学基金 (BK20130155)，江苏省产学研前瞻性联合研究项目(BY2013015- 02)，中央高校基本科研业务费专项资金资助重点项目(JUSRP 51614A)

计量
- 文章访问数: 1574
- HTML全文浏览量: 163
- PDF下载量: 441
- 被引次数: 17
出版历程
- 收稿日期: 2015-10-29
- 修回日期: 2016-03-15
- 刊出日期: 2016-08-19

Multi-view TSK Fuzzy System via Collaborative Learning

Funds:

The National Natural Science Foundation of China (61300151), The Natural Science Foundation of Jiangsu Province (BK20130155), The RD Frontier Grant of Jiangsu Province (BY2013015-02), The Fundamental Research Funds for the Central Universities (JUSRP51614A)

摘要

摘要: 传统模糊系统建模方法本质上是一种单视角学习模式，面向适合多视角处理的场景时，它们通常只能将每一视角割裂开来进行独立建模，这导致其所得系统泛化性能往往不令人满意。针对此缺陷，该文探讨具备多视角学习能力的模糊系统建模方法。为此，基于经典的L2型TSK模糊系统，通过引入具备多视角学习能力的协同学习项，该文提出了核心的多视角TSK型模糊系统(MV-TSK-FS)建模方法。MV-TSK-FS不仅能有效地利用各视角不同特征构成的独立样本信息，还能充分地利用各视角间由于相互关联而存在内在信息，以最终达到提高系统泛化性能的效果。在模拟数据集与真实数据集上的实验结果验证了较之于传统单视角模糊建模方法该多视角模糊系统有着更好的泛化性和适用性。
- 多视角学习 /
- 协同学习 /
- 模糊建模 /
- TSK型模糊系统
Abstract: Conventional fuzzy system modeling methods essentially belong to the single-view learning modality. In multi-view-oriented data scenarios, they can only cope with each view separately, which is prone to incurring their unsatisfactory generalization performance. In response to such problem, the fuzzy system modeling method with the ability of multi-view learning is pursued. To this end, based on the classic L2 norm Takagi-Sugeno-Kang (TSK) fuzzy system, by means of the collaborative learning items qualified for multi-view learning, the core Multi-View TSK Fuzzy System (MV-TSK-FS) modeling method is presented. MV-TSK-FS can not only effectively utilize the independent components composed of the characteristics affiliated to each view, but also take full advantage of the potential information occurred by the interrelated effects among views, which eventually facilitates its relatively strong generalization ability. The experimental results performed on both synthetic and real-life datasets indicate that, compared with some traditional single-view methods, this propounded multi-view fuzzy modeling system owns preferable applicability as well as generalization.
- Multi-view learning /
- Collaborative learning /
- Fuzzy modeling /
- Takagi-Sugeno-Kang (TSK) type fuzzy system

HTML全文

参考文献(17)

LI Guangxia, CHANG Kuiyu, and HOI S C H. Multiview semi-supervised learning with consensus[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(11): 2040-2051. doi: 10.1109/TKDE.2011.160.

HONG Chaoqun, YU Jun, YOU Jane, et al. Multi-view ensemble manifold regularization for 3D object recognition [J]. Information Sciences, 2015, 320: 395-405. doi: 10.1016/ j.ins.2015.03.032.

ZHANG Shunli, YU Xin, SUI Yao, et al. Object tracking with multi-view support vector machines[J]. IEEE Transactions on Multimedia, 2015, 17(3): 265-278. doi: 10.1109/TMM. 2015.2390044.

蒋亦樟, 邓赵红, 王骏, 等. 熵加权多视角协同划分模糊聚类算法[J]. 软件学报, 2014, 25(10): 2293-2311. doi: 10.13328/j. cnki.jos.004510.

JIANG Yizhang, DENG Zhaohong, WANG Jun, et al. Collaborative partition multi-view fuzzy clustering algorithm using entropy weighting[J]. Journal of Software, 2014, 25(10): 2293-2311. doi: 10.13328/j.cnki.jos.004510.

TZORTZIS G F and LIKAS A C. Multiple view clustering using a weighted combination of exemplar-based mixture models[J]. IEEE Transactions on Neural Network, 2010, 21(12): 1925-1938. doi: 10.1109/TNN.2010.2081999.

JIANG Yizhang, CHUNG Fulai, WANG Shitong, et al. Collaborative fuzzy clustering from multiple weighted views[J]. IEEE Transactions on Cybernetics, 2015, 45(4): 688-701. doi: 10.1109/TCYB.2014.2334595.

MERUGU S, ROSSET S, and PERLICH C. A new multi- view regression approach with an application to customer wallet estimation[C]. Proceedings of 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, 2006: 656-661.

DENG Zhaohong, JIANG Yizhang, CHOI Kupsze, et al. Knowledge-leverage-based TSK fuzzy system modeling[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(8): 1200-1212. doi: 10.1109/FUZZ-IEEE. 2014.6891544.

STRM K J and MCAVOY T J. Intelligent control[J]. Journal of Process Control, 1992, 2(3): 115-127.

邓赵红, 张江滨, 蒋亦樟, 等. 基于模糊子空间聚类的〇阶L2型TSK模糊系统[J]. 电子与信息学报, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074.

DENG Zhaohong, ZHANG Jiangbin, JIANG Yizhang, et al. Fuzzy subspace clustering based zero-order L2-norm TSK fuzzy system[J]. Journal of Electronics Information Technology, 2015, 37(9): 2082-2088.

TAKAGI T and SUGENO M. Fuzzy identification of systems and its application to modeling and control[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(1): 116-132. doi: 10.1109/TSMC.1985.6313399.

MAMDANI E H. Application of fuzzy logic to approximate reasoning using linguistic synthesis[J]. IEEE Transactions on Computers, 1977, 26(12): 1182-1191. doi: 10.1109/TC.1977. 1674779.

AZEEM M F, HANMANDLU M, and AHMAD N. Generalization of adaptive neural-fuzzy inference systems[J]. IEEE Transactions on Neural Networks, 2000, 11(6): 1332-1346. doi: 10.1109/72.883438.

JIANG Yizhang, CHUNG Fulai, and WANG Shitong. Enhanced fuzzy partitions vs data randomness in FCM[J]. Journal of Intelligent and Fuzzy Systems, 2014, 27(4): 1639-1648. doi: 10.3233/IFS-141130.

ITO K and NAKANO R. Optimizing support vector regression hyperparameters based on cross-validation[C]. Proceedings of the International Joint Conference on Neural Networks, Jantzen Beach, Portland, Oregon, 2003: 2077-2082.

施引文献

期刊类型引用(9)

1.	蒋卓材，冯贤文. 基于多频分层异构的LTE网络重选切换问题及优化参数选择. 桂林航天工业学院学报. 2021(03): 271-275 . 百度学术
2.	刘晓娟，徐亚顺. FAO系统车地无线通信干扰抑制方案. 计算机工程与设计. 2019(07): 1829-1834+1845 . 百度学术
3.	王瑞峰，席皓哲，姚军娟，吝天锁，郭博迪，臧浩月. 基于位置信息与波束赋形辅助的LTE-R切换算法研究. 云南大学学报(自然科学版). 2019(06): 1137-1143 . 百度学术
4.	高明华，许丽金，杨根红. LTE系统软频率复用技术分析. 计算机仿真. 2018(07): 125-129 . 百度学术
5.	徐林林，朱晓荣. 多运营商频谱共享模型和租赁策略. 电子与信息学报. 2017(03): 582-589 . 本站查看
6.	邱钊洋，黄焱，欧阳喜. 基于折叠相位的猝发信号载波相差盲估计. 电子与信息学报. 2017(07): 1651-1657 . 本站查看
7.	段瑞杰，姚富强，李永贵，郭鹏程. 基于智能跳频的短波无线接入网动态频谱分配算法. 计算机应用. 2016(08): 2163-2169 . 百度学术
8.	黄丽，方红斌. 非对称认知网络中分布式多用户频谱共享算法. 中南民族大学学报(自然科学版). 2016(03): 111-116 . 百度学术
9.	米根锁，马硕梅. 基于速度触发的提前切换算法在LTE-R中的应用研究. 电子与信息学报. 2015(12): 2852-2857 . 本站查看