Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

被引:2
作者
Giang, Le Truong [1 ,2 ]
Son, Le Hoang [3 ]
Giang, Nguyen Long [4 ]
Luong, Nguyen Van [1 ]
Lan, Luong Thi Hong [5 ]
Tuan, Tran Manh [5 ]
Thang, Nguyen Truong [4 ]
机构
[1] Hanoi Univ Ind, Ctr Qual Assurance, Hanoi 100000, Vietnam
[2] Vietnam Acad Sci & Technol, Grad Univ Sci & Technol, Hanoi 100000, Vietnam
[3] Vietnam Natl Univ VNU, VNU Informat Technol Inst, Hanoi 700000, Vietnam
[4] Vietnam Acad Sci & Technol, Inst Informat Technol IoIT, Hanoi 100000, Vietnam
[5] Thuyloi Univ, Fac Comp Sci & Engn, Hanoi 116705, Vietnam
关键词
Fuzzy logic; Adaptation models; Remote sensing; Adaptive systems; Time series analysis; Predictive models; Fuzzy sets; Complex fuzzy inference system; remote sensing images; rule pruning; rule-based system; image change detection; SENSOR;
D O I
10.1109/ACCESS.2023.3268059
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fuzzy inference systems, in general, and complex fuzzy inference systems, in particular, play an increasingly important role in many fields, such as change detection, image classification, recognition problems, etc. Despite being the well-known technique to solve with time series data, the rulebase still has the considered limitation because of the directly affecting the results as well as the processing time of these methods. To overcome this limitation, this study proposes an Adaptive spatial complex inference system that can automatically infer and adapt to the new remotely sensed image. In the proposed model, to predict the image of time t + 1, the system will generate a new rulebase according to this expected image. This new rulebase and the previous Co-Spatial-CFIS+ rulebase are evaluated using a complex fuzzy measure. This measure is built by determining the intersection domain between two rule spaces; this intersection value estimates removing, merging, or adding a newly generated rule into the current rulebase. Finally, a more suitable set of rules is obtained for image prediction. To illustrate the efficiency of the proposed approach, it is applied to the remote sensing cloud image data of the U.S. Navy. Our model evaluated the model's effectiveness in comparison to the state-of-the-art along studies in detecting changes in remote sensing cloud images. Moreover, the findings of the experiments revealed that the proposed model could improve the change detection results in terms of R-2, RMSE, time-consuming, and the number of rules.
引用
收藏
页码:39333 / 39350
页数:18
相关论文
共 43 条
[1]   Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data-A Machine Learning Approach [J].
Ali, Iftikhar ;
Cawkwell, Fiona ;
Dwyer, Edward ;
Green, Stuart .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2017, 10 (07) :3254-3264
[2]   Interval Complex Neutrosophic Set: Formulation and Applications in Decision-Making [J].
Ali, Mumtaz ;
Luu Quoc Dat ;
Le Hoang Son ;
Smarandache, Florentin .
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2018, 20 (03) :986-999
[3]   Complex neutrosophic set [J].
Ali, Mumtaz ;
Smarandache, Florentin .
NEURAL COMPUTING & APPLICATIONS, 2017, 28 (07) :1817-1834
[4]   Distributed secure state estimation for cyber-physical systems under sensor attacks [J].
An, Liwei ;
Yang, Guang-Hong .
AUTOMATICA, 2019, 107 :526-538
[5]  
Bajwa MS, 2015, 2015 2ND INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT (INDIACOM), P1723
[6]  
Borji A., 2007, INT J INTELL TECHNOL, V2, P471
[7]  
Castellano G., 2021, P WILF
[8]   ANCFIS: A Neurofuzzy Architecture Employing Complex Fuzzy Sets [J].
Chen, Zhifei ;
Aghakhani, Sara ;
Man, James ;
Dick, Scott .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2011, 19 (02) :305-322
[9]   Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images [J].
Du, Bo ;
Ru, Lixiang ;
Wu, Chen ;
Zhang, Liangpei .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (12) :9976-9992
[10]   A Fast and Accurate Rule-Base Generation Method for Mamdani Fuzzy Systems [J].
Dutu, Liviu-Cristian ;
Mauris, Gilles ;
Bolon, Philippe .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (02) :715-733