Results 251 to 260 of about 5,787,644 (326)
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Adaptive rule monitoring system
Proceedings of the 1st International Workshop on Software Engineering for Cognitive Services, 2018Rule-based techniques are gaining importance with their ability to augment large scale data processing systems. However, there still remain key challenges amongst current rule-based techniques, including rule monitoring, adapting and evaluation. Among these challenges, monitoring the precision of rules is highly important as it enables analysts to ...
Alireza Tabebordbar, Amin Beheshti
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ETransportation, 2020
A scalable, causal, adaptive rule-based energy management strategy for fuel cell hybrid trains is developed. The rules of this strategy are initiated by the results of two-dimensional dynamic programming under different driving conditions and utilize the
Hujun Peng +6 more
semanticscholar +3 more sources
A scalable, causal, adaptive rule-based energy management strategy for fuel cell hybrid trains is developed. The rules of this strategy are initiated by the results of two-dimensional dynamic programming under different driving conditions and utilize the
Hujun Peng +6 more
semanticscholar +3 more sources
Adaptive Rule Adaptation in Unstructured and Dynamic Environments
WISE, 2019Rule-based systems have been used to augment machine learning based algorithms for annotating data in unstructured and dynamic environments. Rules can alleviate many of shortcomings inherent in pure algorithmic approaches. Rule adaptation is a challenging and error-prone task: in a rule-based system, there is a need for an analyst to adapt rules in ...
Alireza Tabebordbar +3 more
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Granular Flow Graph, Adaptive Rule Generation and Tracking
IEEE Transactions on Cybernetics, 2017A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking.
S. Pal, D. Chakraborty
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A dynamic ensemble outlier detection model based on an adaptive k-nearest neighbor rule
Information Fusion, 2020Ensembles of outlier detectors are drawing increasing attentions recently, in spite of the difficulty on developing ensembles in the framework of unsupervised learning.
Biao Wang, Zhizhong Mao
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FlowStat: Adaptive Flow-Rule Placement for Per-Flow Statistics in SDN
IEEE Journal on Selected Areas in Communications, 2019In this paper, we propose an adaptive flow-rule placement scheme, FlowStat, in a software-defined network (SDN) with an aim to provide per-flow statistics to SDN controller while enhancing overall network performance.
Samaresh Bera, S. Misra, A. Jamalipour
semanticscholar +3 more sources
Rule-based Adaptation of Web Information Systems
World Wide Web, 2007Mobile devices provide a variety of ways to access information resources available on the Web and a high level of adaptability to different aspects of the context (such as the device capabilities, the network QoS, the user preferences, and the location) is strongly required in this scenario.
De Virgilio, R. +2 more
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Adaptive compandor design using the boundary adaptation rule
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994In our previous paper (1993), we introduced a novel unsupervised learning rule for scalar quantization, called the boundary adaptation rule (BAR). Adaptive quantizers were built using the maximization of information-theoretic entropy as a design criterion.
M.M. Van Hulle, D. Martinez
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Calculating sharp adaptation rules
Information Processing Letters, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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IEEE Transactions on Automatic Control, 1991
Summary: This paper presents a new method for nonlinear function identification and application to learning control. The control objective is to identify and compensate for a nonlinear disturbance function. The nonlinear disturbance function is presented as an integral of a predefined kernel function multiplied by an unknown influence function ...
Messner, William +3 more
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Summary: This paper presents a new method for nonlinear function identification and application to learning control. The control objective is to identify and compensate for a nonlinear disturbance function. The nonlinear disturbance function is presented as an integral of a predefined kernel function multiplied by an unknown influence function ...
Messner, William +3 more
openaire +2 more sources