基于張量分解的衛(wèi)星遙測(cè)缺失數(shù)據(jù)預(yù)測(cè)算法
doi: 10.11999/JEIT180728
-
國(guó)家衛(wèi)星氣象中心 北京 100081
Missing Telemetry Data Prediction Algorithm via Tensor Factorization
-
National Satellite Meteorological Center, Beijing 100081, China
-
摘要:
衛(wèi)星健康狀況監(jiān)測(cè)是衛(wèi)星安全保障的重要基礎(chǔ)，而衛(wèi)星遙測(cè)數(shù)據(jù)又是衛(wèi)星健康狀況分析的唯一數(shù)據(jù)來(lái)源。因此，衛(wèi)星遙測(cè)缺失數(shù)據(jù)的準(zhǔn)確預(yù)測(cè)是衛(wèi)星健康分析的重要前瞻性手段。針對(duì)極軌衛(wèi)星多組成系統(tǒng)、多儀器載荷以及多監(jiān)測(cè)指標(biāo)形成的高維數(shù)據(jù)特點(diǎn)，該文提出一種基于張量分解的衛(wèi)星遙測(cè)缺失數(shù)據(jù)預(yù)測(cè)算法(TFP)，以解決當(dāng)前數(shù)據(jù)預(yù)測(cè)方法大多面向低維數(shù)據(jù)或只能針對(duì)特定維度的不足。所提算法將遙測(cè)數(shù)據(jù)中的系統(tǒng)、載荷、指標(biāo)以及時(shí)間等多維因素作為統(tǒng)一的整體進(jìn)行張量建模，以完整、準(zhǔn)確地表達(dá)數(shù)據(jù)的高維特征；其次，通過(guò)張量分解計(jì)算數(shù)據(jù)模型的成分特征，通過(guò)成分特征可對(duì)張量模型進(jìn)行準(zhǔn)確重構(gòu)，并在重構(gòu)過(guò)程中對(duì)缺失數(shù)據(jù)進(jìn)行準(zhǔn)確預(yù)測(cè)；最后，提出一種高效的優(yōu)化算法實(shí)現(xiàn)相關(guān)的張量計(jì)算，并對(duì)算法中最優(yōu)參數(shù)設(shè)置進(jìn)行嚴(yán)格的理論推導(dǎo)。實(shí)驗(yàn)結(jié)果表明，所提算法的預(yù)測(cè)準(zhǔn)確度優(yōu)于當(dāng)前大部分預(yù)測(cè)算法。
-
關(guān)鍵詞:
- 極軌衛(wèi)星 /
- 遙測(cè)數(shù)據(jù) /
- 缺失數(shù)據(jù)預(yù)測(cè) /
- 張量分解
Abstract:Satellite health monitoring is an important concern for satellite security, for which satellite telemetry data is the only source of data. Therefore, accurate prediction of missing data of satellite telemetry is an important forward-looking approach for satellite health diagnosis. For the high-dimensional structure formed by the satellite multi-component system, multi-instrument and multi-monitoring index, the Tensor Factorization based Prediction (TFP) algorithm for missing telemetry data is proposed. The proposed algorithm surpasses most existing methods, which can only be applied to low-dimensional data or specific dimension. The proposed algorithm makes accurate predictions by modeling the telemetry data as a Tensor to integrally utilize its high-dimensional feature; Computing the component matrixes via Tensor Factorization to reconstruct the Tensor which gives the predictions of the missing data; An efficient optimization algorithm is proposed to implement the related tensor calculations, for which the optimal parameter settings are strictly theoretically deduced. Experiments show that the proposed algorithm has better prediction accuracy than the most existing algorithms.
-
Key words:
- Satellite /
- Telemetry data /
- Missing data prediction /
- Tensor factorization
-
算法1：TFP算法 輸入：數(shù)據(jù)集$ {\cal X}\in {{\mathbb{R}}^{{{I}_{1}}\times {{I}_{2}}\times \cdots \times {{I}_{N}}}}$； 輸出：訓(xùn)練后的成分矩陣$ {{ A}^{\left(j \right)}}$ (j=1 to N) 隨機(jī)初始化成分矩陣$ {{ A}^{\left( j \right)}}$(j=1 to N) Repeat For each $ { A}_{{i_j}r}^{\left( j \right)}\left( {1 \le j \le N,1 \le {i_j} \le {I_j},1 \le r \le R} \right)$ If $ g_{{i_j}r}^{\left( j \right)}{|_t} \cdot g_{{i_j}r}^{\left( j \right)}{|_{t - 1}} > 0$ $ \delta _{ {i_j}r}^{\left( j \right)}{|_t} = {\rm{min} }\left( {\delta _{ {i_j}r}^{\left( j \right)}{|_{t - 1} } \cdot {\eta ^ + },{\rm{MaxSize}}} \right)$ $ { A}_{{i_j}r}^{\left( j \right)}{|_{t + 1}} = { A}_{{i_j}r}^{\left( j \right)}{|_t} - {\rm{sign}}\left( {g_{{i_j}r}^{\left( j \right)}{|_t}} \right) \cdot \delta _{{i_j}r}^{\left( j \right)}{|_t}$ Else If $ g_{{i_j}r}^{\left( j \right)} \cdot g_{{i_j}r}^{\left( j \right)}{\rm{'}} < 0$ $ \delta _{ {i_j}r}^{\left( j \right)}{|_t} = {\rm{max} }\left( {\delta _{ {i_j}r}^{\left( j \right)}{|_{t - 1} } \cdot {\eta ^ - },{\rm {MinSize}}} \right)$ If $ L{|_t} > L{|_{t - 1}}$ $ { A}_{{i_j}r}^{\left( j \right)}{|_{t + 1}} = { A}_{{i_j}r}^{\left( j \right)}{|_t} + {\rm{sign}}\left( {g_{{i_j}r}^{\left( j \right)}{|_{t - 1}}} \right) \cdot \delta _{{i_j}r}^{\left( j \right)}{|_{t - 1}}$ $ L{|_t} = 0$ End If Else $ \delta _{{i_j}r}^{\left( j \right)}{|_t} = \delta _{{i_j}r}^{\left( j \right)}{|_{t - 1}}$ $ { A}_{{i_j}r}^{\left( j \right)}{|_{t + 1}} = { A}_{{i_j}r}^{\left( j \right)}{|_t} - {\rm{sign}}\left( {g_{{i_j}r}^{\left( j \right)}{|_t}} \right) \cdot \delta _{{i_j}r}^{\left( j \right)}{|_t}$ End If End For Until $ L \le \varepsilon $ or maximum iterations exhausted 
下載: 導(dǎo)出CSV
表 1 TFP算法與其它5個(gè)方法的對(duì)比
方法 數(shù)據(jù)密度5% 數(shù)據(jù)密度10% 數(shù)據(jù)密度20% 數(shù)據(jù)密度50% MAE RMSE MAE RMSE MAE RMSE MAE RMSE NMF 0.6175 1.5789 0.6007 1.5485 0.5986 1.5233 0.4870 1.4847 PMF 0.5687 1.4792 0.4984 1.2842 0.4492 1.1855 0.4006 1.0820 UPCC 0.6204 1.4010 0.5513 1.3139 0.4875 1.2343 0.3114 1.0749 IPCC 0.6886 1.4278 0.5908 1.3245 0.4454 1.2094 0.2895 1.1724 TA 0.6239 1.4058 0.5360 1.3045 0.4496 1.2030 0.2106 1.0988 TFP 0.3815 0.9469 0.3073 0.7597 0.2270 0.5619 0.1235 0.3150
下載: 導(dǎo)出CSV
-
李平, 張路遙, 曹霞, 等. 基于潛在主題的混合上下文推薦算法[J]. 電子與信息學(xué)報(bào), 2018, 40(4): 957–963. doi: 10.11999/JEIT170623LI Ping, ZHANG Luyao, CAO Xia, et al. Hybrid context recommendation algorithm based on latent topic[J]. Journal of Electronics &Information Technology, 2018, 40(4): 957–963. doi: 10.11999/JEIT170623 CHEN I F and LU Chijie. Sales forecasting by combining clustering and machine-learning techniques for computer retailing[J]. Neural Computing and Applications, 2017, 28(9): 2633–2647. doi: 10.1007/s00521-016-2215-x MA You, WANG Shangguang, HUNG P C K, et al. A highly accurate prediction algorithm for unknown Web service QoS values[J]. IEEE Transactions on Services Computing, 2016, 9(4): 511–523. doi: 10.1109/TSC.2015.2407877 馬友, 王尚廣, 孫其博, 等. 一種綜合考慮主客觀權(quán)重的Web服務(wù)QoS度量算法[J]. 軟件學(xué)報(bào), 2014, 25(11): 2473–2485. doi: 10.13328/j.cnki.jos.004508MA You, WANG Shangguang, SUN Qibo, et al. Web service quality metric algorithm employing objective and subjective weight[J]. Journal of Software, 2014, 25(11): 2473–2485. doi: 10.13328/j.cnki.jos.004508 DING Shuai, LI Yeqing, WU Desheng, et al. Time-aware cloud service recommendation using similarity-enhanced collaborative filtering and ARIMA model[J]. Decision Support Systems, 2018, 107: 103–115. doi: 10.1016/j.dss.2017.12.012 KUANG Li, YU Long, HUANG Lan, et al. A personalized QoS prediction approach for CPS service recommendation based on reputation and location-aware collaborative filtering[J]. Sensors, 2018, 18(5): 1556. doi: 10.3390/s18051556 COLOMO-PALACIOS R, GARCíA-PE?ALVO F J, STANTCHEV V, et al. Towards a social and context-aware mobile recommendation system for tourism[J]. Pervasive and Mobile Computing, 2017, 38: 505–515. doi: 10.1016/j.pmcj.2016.03.001 IGEL C and HüSKEN M. Improving the Rprop learning algorithm[C]. The 2nd International Symposium on Neural Computation, Berlin, Germany, 2000: 115–121. GLIGORIJEVI? V, PANAGAKIS Y, and ZAFEIRIOU S. Non-negative matrix factorizations for multiplex network analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(4): 928–940. doi: 10.1109/TPAMI.2018.2821146 MA Wenping, WU Yue, and GONG Maoguo. Local probabilistic matrix factorization for personal recommendation[C]. The 13th International Conference on Computational Intelligence and Security, Hong Kong, China, 2017: 97–101. doi: 10.1109/CIS.2017.00029 SHAO Lingshuang, ZHANG Jing, WEI Yong, et al. Personalized QoS prediction for web services via collaborative filtering[C]. The IEEE International Conference on Web Services, Salt Lake City, USA, 2007: 439–446. doi: 10.1109/ICWS.2007.140. SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]. The 10th International Conference on World Wide Web, Hong Kong, China, 2001: 285–295. doi: 10.1145/371920.372071. KANG M G and KATSAGGELOS A K. General choice of the regularization functional in regularized image restoration[J]. IEEE Transactions on Image Processing, 1995, 4(5): 594–602. doi: 10.1109/83.382494 KATSAGGELOS A K, BIEMOND J, SCHAFER R W, et al. A regularized iterative image restoration algorithm[J]. IEEE Transactions on Signal Processing, 1991, 39(4): 914–929. doi: 10.1109/78.80914 MILLER K. Least squares methods for ill-posed problems with a prescribed bound[J]. SIAM Journal on Mathematical Analysis, 1970, 1(1): 52–74. doi: 10.1137/0501006 KOLDA T G and BADER B W. Tensor decompositions and applications[J]. SIAM Review, 2009, 51(3): 455–500. doi: 10.1137/07070111X COMON P, TEN BERGE J M, DE LATHAUWER L, et al. Generic and typical ranks of multi-way arrays[J]. Linear Algebra and Its Applications, 2009, 430(11/12): 2997–3007. doi: 10.1016/j.laa.2009.01.014 -
計(jì)量
- 文章訪問(wèn)數(shù): 2857
- HTML全文瀏覽量: 1337
- PDF下載量: 104
- 被引次數(shù): 0
下載:
