shafer 中文意思是什麼

shafer 解釋
謝弗
  1. From the work mentioned above, the paper independently gives a new method to generate belief functions based on rough set. and it is accordant with the requirement of dempster - shafer evidence theory

    作者還獨立提出了一種基於粗糙集理論的信任函數構造方法，並分析證明了其完全符合證據理論的要求。
  2. The fusion of stereo vision and laser - radar ( ladar ) is also studied preliminarily in the background of alv navigation. by introducing the ladar data into the height prediction before matching, the fusion can occur on data layer. we have also found and practiced a feature layer fusion method based on dempster - shafer evidential reasoning approach

    將激光雷達的測量結果引入立體匹配前的高度預測中，提出了立體視覺與激光雷達在數據層上的信息融合方法;基於d - s證據理論，提出了立體視覺與激光雷達在特徵層上的信息融合方法。
  3. Research on fault diagnosis method based on dempster - shafer evidential theory

    證據理論的信息融合在設備故障診斷中應用
  4. Trust evaluation model based on dempster - shafer evidence theory

    證據理論的信任評估模型
  5. Research on the evaluation of risk - return based on dempster - shafer theory

    基於證據理論的風險收益評價模型及其應用
  6. The application of multisensor information fusion of dempster - shafer evidential reasoning to sliding mode control with measuring noise was investigated, which helps to depress the uncertainty of the sliding mode function

    摘要研究了d - s證據推理多傳感器信息融合方法在存在量測噪聲的滑模控制中的應用，它有助於削弱模函數的不確定性。
  7. Furthermore, dempster - shafer ( d - s ) method which is used to identify objects in multi - sensor data fusion is elaborated. different situations in homogeneous and

    此外，該文還詳細闡明了在多傳感器數據融合中目標識別的證據推理方法，對同類證據及不同類證據融合識別的各種情況分別進行了分析和討論。
  8. The dada fusion technique of the dempster - shafer evidence theory is described systematically and applied in the target detection of ship. on the base of studying and simulating the through signatures of the sound field, the magnetic field and the hydraulic field of ship, the method of constructing basic probability assignment of sensor is analyzed. then the application of target detection of ship based on the dempster - shafer evidence theory is successfully simulated, and some conclusions, with reason, is drawn

    本文介紹了數據融合技術的基本概念、建模思想及研究內容，系統地研究了d - s （ dempster - shafer ）證據推理理論的數據融合技術及其在艦船目標檢測中的應用，並在艦船聲場、磁場和水壓場通過特性的基礎上構造出各傳感器的基本概率分配函數bpa （ basicprobabilityassignment ） ，然後以此為基礎將d - s證據推理理論成功地應用於艦船目標檢測的模擬，並得到比較理想的模擬結果。
  9. Several results of dempster - shafer ( d - s ) method applied to target fusion identification of two sensors and conclusions drawn from them were given and proved. the recurrence formula of multi - sensor ( > 2 ) d - s fusion identification were derived and their properties were shown clearly

    並針對多傳感器的目標識別問題，文中給出並證明了兩個傳感器dempster - shafer （ d - s ）融合識別同一目標時的若干結論及其歸納的結論，同時推出了多（ 2 ）傳感器dempster - shafer融合識別同一目標時的遞推式，並分析了它們的性質。
  10. Due to the two different tasks of obstacle detection : recognition of obstacles from roadsides and accurate positioning the obstacles, the dempster - shafer evidence theory based identification and extend kalman filtering based target tracking technique were adopted respectively

    具體地說，是採用了基於d - s證據理論的身份識別技術和基於擴展卡爾曼濾波的目標跟蹤技術，來分別完成障礙檢測中障礙和路邊的識別以及障礙準確位置的確定兩大主要任務。
  11. How to use dempster - shafer ( d - s ) method to solve multi - sensor data fusion problems is analyzed in this paper. based on basic probability assignment of target type decided by multiple sensors, new sensor data are added continually, and believe function and plausibility function are update ; finally the destination of decision of target type is arrived

    應用證據理論（ d - s方法） ，解在多傳感器條件下的數據融合問題，具體方法是根據多個傳感器對目標類型判斷的基本概率分配函數，不斷添加新的傳感器數據，更新信任函數和似然函數，最終判斷目標類型。
  12. The probabilistic approaches include the belief network, the dynamic causality diagram, the markov network, the approach used in prospector, etc. the non - probabilistic approaches include the certainty factor theory in mycin, fuzzy set logic, dempster - shafer theory, etc. the non - probabilistic approaches have reached some achievement in their respective application domain, and shown their shortage while applying

    另一類是非概率的方法，包括mycin的可信度因子（ certaintyfactor ） 、模糊邏輯（ fuzzylogic ）以及dempster - shafer的證據理論等。非概率的方法雖然在各自的應用領域都取得了一定成果，但在運用過程中人們越來越意識到這類方法的不足。
  13. This thesis mainly discussed the theoretical basis of information fusion, the applications and state - of - the - art. then studied it ' s applications on target recognition. we introduced dempster - shafer inference theory and neural network techniques, which were two typical algorithms in information fusion in details

    本文主要研究信息融合的理論基礎、相關應用問題以及研究現狀和發展方向，討論了信息融合技術在目標識別中的應用問題以及相關演算法，重點介紹了d ? s證據理論和神經網路技術這兩種融合演算法。
  14. Time - space data fusion and object recognition based on matrix analysis and dempster - shafer evidence theory

    證據理論的時空數據融合及目標識別
  15. Two popular statistical - based techniques, namely, bayes and dempster - shafer methods are applied to develop radar target identification algorithms for our application. the performance of bayes method and d - s method is compared in convergence time and robustness

    運用兩種普遍使剛的統計推斷方法? bayes方法和d - s證據理論對以上空中目標進行識別，比較了它們的收斂時間和穩健性。
  16. Based on a mathematical theory of evidence produced by g. shafer, the d - s information fusion method for the determination of rockmass mechanical parameters is presented for the first time, by which the discernment frame of rockmass mechanical parameters is established

    （ 3 ）提出並研究了邊坡穩定性分析中存在的未確知性問題。以證據理論為基礎，首次提出基於證據理論的巖體力學參數d - s信息融合方法。
  17. According to the present situation and developing trends on condition monitoring and fault diagnosing of hydroelectric sets at home and abroad, and taking the technique of data fusion diagnosis hydroelectric sets as theme, this dissertation researches into data fusion fault diagnosis method based on dempster - shafer theory, and applies this technique to hydroelectric sets vibration fault diagnoses for the first time, and presents that using sub - band energy of vibration frequency to reflect the vibration intensity of hydroelectric sets

    本文根據國內外關於水電機組狀態監測與故障診斷研究的現狀與發展趨勢，以機組振動故障的信息融合診斷技術為研究主題，對基於dempster - shafer證據理論的信息融合故障診斷方法進行研究，首次將該方法應用到水電機組振動故障診斷中，並提出了用機組振動各頻率的子帶能量反映其振動強度的方法。
  18. We researched on a new neural network shape recognition system based on dempster - shafer theory, which integrated the advantages of d - s theory and neural network

    為了驗證我們的思想，研究了基於d ? s證據理論的神經網路形狀識別系統。
  19. Multisensor information is fused in temporal field by combing dempster - shafer theory and neural networks in order to conduct recognition and classification tasks

    將dempster - shafer理論與神經網路相結合，在時間域對多傳感器的多次測量進行融合，以進行識別分類。
  20. Algorithms to combine the neural networks classifiers based on dempster - shafer theory and two kinds of fuzzy integral ( sugeno and choquet integral ) respectively are proposed. the influences of the fact that every classifier has different classification ability for different class are all considered in these two kinds of algorithms

    提出了分別基於dempster - shafer組合公式和兩類模糊積分（ sugeno積分和choquet積分）進行多個神經網路分類器組合的演算法，這兩種演算法都考慮了每個分類器對不同類的識別能力的不同這一經驗知識。
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