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排序方式: 共有316条查询结果,搜索用时 171 毫秒
1.
针对模拟电路健康管理的特点,提出了一种基于PSO优化多核RVM的模拟电路故障预测方法。利用参数分析得到电路的输出频域响应作为特征,计算其与电路无故障标准响应的欧氏距离来表征电路元件健康值,将多个核函数线性组合,并用PSO优化多核RVM参数后的模型实现对各个时间点元件的健康值变化轨迹进行预测。仿真结果表明,该方法在小样本情况下,预测效果优于单一核函数的RVM模型,适用于健康管理中实时预测,具有较好的实用性。 相似文献
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The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of modern application domains, providing both power and interpretability via uncertainty analysis. This article introduces and discusses two methods which straddle the areas of probabilistic Bayesian schemes and kernel methods for regression: Gaussian Processes and Relevance Vector Machines. Our focus is on developing a common framework with which to view these methods, via intermediate methods a probabilistic version of the well-known kernel ridge regression, and drawing connections among them, via dual formulations, and discussion of their application in the context of major tasks: regression, smoothing, interpolation, and filtering. Overall, we provide understanding of the mathematical concepts behind these models, and we summarize and discuss in depth different interpretations and highlight the relationship to other methods, such as linear kernel smoothers, Kalman filtering and Fourier approximations. Throughout, we provide numerous figures to promote understanding, and we make numerous recommendations to practitioners. Benefits and drawbacks of the different techniques are highlighted. To our knowledge, this is the most in-depth study of its kind to date focused on these two methods, and will be relevant to theoretical understanding and practitioners throughout the domains of data-science, signal processing, machine learning, and artificial intelligence in general. 相似文献
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Nonlinear model predictive control with relevance vector regression and particle swarm optimization 总被引:1,自引:0,他引:1
In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression (RVR) and particle swarm optimization with controllable random exploration velocity (PSO-CREV) is applied to a catalytic continuous stirred tank reactor (CSTR) process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function (RBF) kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control (MPC) using a deterministic sparse kernel learning technique called Least squares support vector machines (LS-SVM) regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control. 相似文献
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An accurate and rapid method is required to retrieve the overwhelming majority of digital images. To date, image retrieval methods include content-based retrieval and keyword-based retrieval, the former utilizing visual features such as color and brightness, and the latter utilizing keywords that describe the image. However, the effectiveness of these methods in providing the exact images the user wants has been under scrutiny. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as feedback during the retrieval session in order to define a user’s need and provide an improved result. Methods that employ relevance feedback, however, do have drawbacks because several pieces of feedback are necessary to produce an appropriate result, and the feedback information cannot be reused. In this paper, a novel retrieval model is proposed, which annotates an image with keywords and modifies the confidence level of the keywords in response to the user’s feedback. In the proposed model, not only the images that have been given feedback, but also other images with visual features similar to the features used to distinguish the positive images are subjected to confidence modification. This allows for modification of a large number of images with relatively little feedback, ultimately leading to faster and more accurate retrieval results. An experiment was performed to verify the effectiveness of the proposed model, and the result demonstrated a rapid increase in recall and precision using the same amount of feedback. 相似文献
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为缩小图像的低层特征与高层语义之间的语义鸿沟,基于支持向量机的相关反馈机制受到越来越广泛的关注,但这种方法并没有利用未标记样本的隐含信息.为更好地利用这些信息,提出将直推式支持向量机作为反馈过程中的学习算法.通过分析其所用特征向量的特点,设计一种颜色稀疏特征,并将其与纹理特征结合作为图像描述的特征.实验结果表明该方法较令人满意,同时也说明直推式支持向量机可在文本分类以外的领域取得较好结果. 相似文献
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《The Journal of Strategic Information Systems》2022,31(4):101746
The Journal of Strategic Information Systems (JSIS) was first published December 1991 by Robert (Bob) D. Galliers, its Founding Editor-in-Chief (EiC). Early on Bob invited me to join the JSIS International Editorial Board1 (the Board then being more operational, with members serving as quasi-AEs, an arrangement we’ve reinstituted more recently). I became Pacific Asia Region Editor in 2003, with JSIS shifting from region editors to Senior Editors in 2007, in which role I served the journal through the end of 2018. Mid-2018 Bob and Sirkka (Sirkka Jarvenpaa joined as co-EiC in 2000) jointly decided it was time; and announced they would both step down from their co-EiC roles at the end of that year. In September 2018, I was endorsed by Bob and Sirkka, the Senior Editors and Elsevier, to succeed Bob and Sirkka and assume the role of Editor-in-Chief of JSIS commencing 1 January 2019. I served as Editor-in-Chief through 1 July 2021 after which Yolande Chan and I served as co-Editors-in-Chief. Now the end of 2022, I am stepping down from operational involvement with the journal. In this my final editorial I focus on my time as EiC, also reflecting on my almost 3 decades with the journal. I much appreciate being invited to write these brief ruminations and am honoured to be included amongst the journal’s Emeritus, along with the esteemed Robert Galliers and Sirkka Jarvenpaa. 相似文献
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SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents
Keyword search in XML documents has recently gained a lot of research attention. Given a keyword query, existing approaches first compute the lowest common ancestors (LCAs) or their variants of XML elements that contain the input keywords, and then identify the subtrees rooted at the LCAs as the answer. In this the paper we study how to use the rich structural relationships embedded in XML documents to facilitate the processing of keyword queries. We develop a novel method, called SAIL, to index such structural relationships for efficient XML keyword search. We propose the concept of minimal-cost trees to answer keyword queries and devise structure-aware indices to maintain the structural relationships for efficiently identifying the minimal-cost trees. For effectively and progressively identifying the top-k answers, we develop techniques using link-based relevance ranking and keyword-pair-based ranking. To reduce the index size, we incorporate a numbering scheme, namely schema-aware dewey code, into our structure-aware indices. Experimental results on real data sets show that our method outperforms state-of-the-art approaches significantly, in both answer quality and search efficiency. 相似文献