Pub Date : 2020-03-05DOI: 10.1201/9781420050646.PTG6
A. Refenes, F. Blayo, Magali E. Azema-Barac, D. Bounds, G. Grudnitski, D. Ross
{"title":"Economics, Finance, and Business","authors":"A. Refenes, F. Blayo, Magali E. Azema-Barac, D. Bounds, G. Grudnitski, D. Ross","doi":"10.1201/9781420050646.PTG6","DOIUrl":"https://doi.org/10.1201/9781420050646.PTG6","url":null,"abstract":"","PeriodicalId":165433,"journal":{"name":"Handbook of Fuzzy Computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128266136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enrique H. Ruspini, Piero P Bonissone, Witold Pedrycz
Ultra-tight integration Tracking error In the traditional strapdown inertial navigation system/global positioning system (SINS/GPS) ultra-tight integration structure, the mutual aiding between SINS and GPS forms a positive feedback loop, through which measurement errors of both subsystems are coupled deeply. In signal jamming or/and dynamic conditions, the Doppler aiding error derived from the SINS using low-grade inertial measurement unit (IMU) can increase rapidly, and cause GPS measurement errors to be correlated with the SINS velocity errors. Such correlations can result in poor estimation accuracy of the integration Kalman filter, losing lock of tracking loops or even yielding system instability. To solve this problem, we propose to model tracking errors of the SINS aided phase lock loop and to derive a new tracking-error estimator. Then, an innovative scheme for SINS/GPS ultra-tight integration using low-grade IMU is investigated. Simulations experiments are implemented to verify this innovative scheme under challenging environments.
超紧密集成跟踪误差 在传统的带下惯性导航系统/全球定位系统（SINS/GPS）超紧密集成结构中，SINS 和 GPS 之间的相互辅助形成了一个正反馈回路，两个子系统的测量误差通过该回路深度耦合。在信号干扰或/和动态条件下，使用低级惯性测量单元（IMU）的 SINS 导出的多普勒辅助误差会迅速增加，导致 GPS 测量误差与 SINS 速度误差相关。这种相关性会导致卡尔曼滤波器的估计精度降低，失去跟踪环路的锁定，甚至导致系统不稳定。为解决这一问题，我们建议对 SINS 辅助锁相环的跟踪误差进行建模，并推导出一种新的跟踪误差估计器。然后，我们研究了一种使用低级 IMU 实现 SINS/GPS 超紧密集成的创新方案。模拟实验验证了这一创新方案在具有挑战性的环境中的应用。
{"title":"Aerospace","authors":"Enrique H. Ruspini, Piero P Bonissone, Witold Pedrycz","doi":"10.2307/3959140","DOIUrl":"https://doi.org/10.2307/3959140","url":null,"abstract":"Ultra-tight integration Tracking error In the traditional strapdown inertial navigation system/global positioning system (SINS/GPS) ultra-tight integration structure, the mutual aiding between SINS and GPS forms a positive feedback loop, through which measurement errors of both subsystems are coupled deeply. In signal jamming or/and dynamic conditions, the Doppler aiding error derived from the SINS using low-grade inertial measurement unit (IMU) can increase rapidly, and cause GPS measurement errors to be correlated with the SINS velocity errors. Such correlations can result in poor estimation accuracy of the integration Kalman filter, losing lock of tracking loops or even yielding system instability. To solve this problem, we propose to model tracking errors of the SINS aided phase lock loop and to derive a new tracking-error estimator. Then, an innovative scheme for SINS/GPS ultra-tight integration using low-grade IMU is investigated. Simulations experiments are implemented to verify this innovative scheme under challenging environments.","PeriodicalId":165433,"journal":{"name":"Handbook of Fuzzy Computation","volume":"7 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141224386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-05DOI: 10.1201/9780429142741-79
Enrique H. Ruspini, P. Bonissone, Witold Pedrycz
Multi-view unsupervised feature selection (MUFS) has recently aroused considerable attention, which can select the compact representative feature subset from original multi-view data. Despite the promising preliminary performance, most previous MUFS methods fail to explore the discriminative ability of multi-view data. In addition, they usually utilize spectral analysis to maintain the geometrical structure, which will inevitably increase the difficulty of parameter selection. To address these issues, we present a novel MUFS method, named structural regularization based discriminative multi-view unsupervised feature selection (SDFS). Specifically, we calculate the similarity matrix of sample space from different views and automatically weight each view-specific graph to learn a consensus similarity graph, in which these two types of graphs can promote each other. Further, we treat the learned latent representation as the cluster indicator, and employ a graph regularization without introducing additional parameters to maintain the geometrical structure of data. Besides, a simple yet efficient iterative updating algorithm with theoretical convergence property is developed. Extensive experiments on several benchmark datasets verify that the designed model is superior to several state-of-the-art MUFS models.
{"title":"Knowledge-Based Systems","authors":"Enrique H. Ruspini, P. Bonissone, Witold Pedrycz","doi":"10.1201/9780429142741-79","DOIUrl":"https://doi.org/10.1201/9780429142741-79","url":null,"abstract":"Multi-view unsupervised feature selection (MUFS) has recently aroused considerable attention, which can select the compact representative feature subset from original multi-view data. Despite the promising preliminary performance, most previous MUFS methods fail to explore the discriminative ability of multi-view data. In addition, they usually utilize spectral analysis to maintain the geometrical structure, which will inevitably increase the difficulty of parameter selection. To address these issues, we present a novel MUFS method, named structural regularization based discriminative multi-view unsupervised feature selection (SDFS). Specifically, we calculate the similarity matrix of sample space from different views and automatically weight each view-specific graph to learn a consensus similarity graph, in which these two types of graphs can promote each other. Further, we treat the learned latent representation as the cluster indicator, and employ a graph regularization without introducing additional parameters to maintain the geometrical structure of data. Besides, a simple yet efficient iterative updating algorithm with theoretical convergence property is developed. Extensive experiments on several benchmark datasets verify that the designed model is superior to several state-of-the-art MUFS models.","PeriodicalId":165433,"journal":{"name":"Handbook of Fuzzy Computation","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141224387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-05DOI: 10.1201/9780429142741-160
{"title":"Directions for Future Research","authors":"","doi":"10.1201/9780429142741-160","DOIUrl":"https://doi.org/10.1201/9780429142741-160","url":null,"abstract":"","PeriodicalId":165433,"journal":{"name":"Handbook of Fuzzy Computation","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116518858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-05DOI: 10.1201/9780429142741-39
G. Klir, Bo Yuan
{"title":"Modeling and Simulation","authors":"G. Klir, Bo Yuan","doi":"10.1201/9780429142741-39","DOIUrl":"https://doi.org/10.1201/9780429142741-39","url":null,"abstract":"","PeriodicalId":165433,"journal":{"name":"Handbook of Fuzzy Computation","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117186391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1988-08-01DOI: 10.1201/9780429142741-149
A. Debons
Information science considers the relationships between people, places and technology and the information those interactions yield. The internet is a broad example of a socio-technical system that is comprised of hardware and software, but in daily life is better understood as a constantly changing social infrastructure upon which complex forms of human-human and human-information interaction rest. Scholars and students of information science develop new methods to study these socio-technical phenomena, and translate those findings to the design and development of useful and meaningful technology.
{"title":"Information Science","authors":"A. Debons","doi":"10.1201/9780429142741-149","DOIUrl":"https://doi.org/10.1201/9780429142741-149","url":null,"abstract":"Information science considers the relationships between people, places and technology and the information those interactions yield. The internet is a broad example of a socio-technical system that is comprised of hardware and software, but in daily life is better understood as a constantly changing social infrastructure upon which complex forms of human-human and human-information interaction rest. Scholars and students of information science develop new methods to study these socio-technical phenomena, and translate those findings to the design and development of useful and meaningful technology.","PeriodicalId":165433,"journal":{"name":"Handbook of Fuzzy Computation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114992757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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