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气垫参数辨识与参数优化
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作者 朱九英 张亮 付佳杰 《航天返回与遥感》 CSCD 北大核心 2022年第2期24-36,共13页
气垫具有成本低、环境适应性强等优点,是目前空投与软着陆研究中的热点。气垫设计中材料的固有参数和结构参数决定了气垫的缓冲性能,是设计中的重点问题。文章基于LS-DYNA有限元仿真软件和LS-OPT优化工具提出了一种结合小尺寸试验、全... 气垫具有成本低、环境适应性强等优点,是目前空投与软着陆研究中的热点。气垫设计中材料的固有参数和结构参数决定了气垫的缓冲性能,是设计中的重点问题。文章基于LS-DYNA有限元仿真软件和LS-OPT优化工具提出了一种结合小尺寸试验、全尺寸仿真与非线性映射代理模型的缓冲气垫系统参数辨识与结构参数优化方法。该方法通过小尺寸自由跌落缓冲试验和非线性映射法,辨识出气垫系统固有参数;并通过全尺寸有限元仿真,建立跌落缓冲仿真代理模型;然后,在此基础上对气垫结构参数进行优化以改善缓冲效果。利用该方法对某空投装备气垫缓冲系统进行了参数辨识与参数优化,并将优化设计后的气垫系统用于空投装备缓冲试验。研究表明,基于优化结果进行的仿真分析所得的加速度仿真数据与试验数据两者的曲线过程与形态相似性强,该方法有效。研究方法对缓冲气垫的设计与优化有一定的参考价值。 展开更多
关键词 气垫缓冲 有限元仿真 非线映射法 参数优化 着陆缓冲
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Approximate Analytic Solution of Solitary Wave for a Class of Nonlinear Disturbed Long-Wave System 被引量:5
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作者 莫嘉琪 姚静荪 唐荣荣 《Communications in Theoretical Physics》 SCIE CAS CSCD 2010年第7期27-30,共4页
In this paper, the approximate expressions of the solitary wave solutions for a class of nonlinear disturbed long-wave system are constructed using the homotopie mapping method.
关键词 nonlinear long-wave equation solitary wave approximate analytic solution
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A SPARSE PROJECTION CLUSTERING ALGORITHM 被引量:4
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作者 Xie Zongbo Feng Jiuchao 《Journal of Electronics(China)》 2009年第4期549-551,共3页
A clustering algorithm based on Sparse Projection (SP), called Sparse Projection Clus- tering (SPC), is proposed in this letter. The basic idea is applying SP to project the observed data onto a high-dimensional spars... A clustering algorithm based on Sparse Projection (SP), called Sparse Projection Clus- tering (SPC), is proposed in this letter. The basic idea is applying SP to project the observed data onto a high-dimensional sparse space, which is a nonlinear mapping with an explicit form and the K-means clustering algorithm can be therefore used to explore the inherent data patterns in the new space. The proposed algorithm is applied to cluster a complete artificial dataset and an incomplete real dataset. In comparison with the kernel K-means clustering algorithm, the proposed algorithm is more efficient. 展开更多
关键词 Sparse Projection Clustering (SPC) K-means clustering Kernel K-means clustering
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Power Big Data Fusion Prediction
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作者 Liu Yan Song Yu +1 位作者 Li Gang Liang Weiqiang 《Computer Technology and Application》 2016年第3期165-171,共7页
This paper is a research on the characteristics of power big data. According to the characteristics of "large volume", "species diversity", "sparse value density", "fast speed" of the power big data, a predict... This paper is a research on the characteristics of power big data. According to the characteristics of "large volume", "species diversity", "sparse value density", "fast speed" of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm. 展开更多
关键词 Power big data fusion prediction Map Reduce BP neural network.
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