reducing fusion 中文意思是什麼
reducing fusion
解釋
還原性熔化-
It has been shown that when the partially coherent beams propagate in the atmosphere, it may be less affected by turbulence than are fully coherent ones. moreover in laser fusion a highly coherent beam is transformed into a partially coherent beam, for reducing the speckle and for getting more smooth focused spot
例如，部分相干光在大氣中傳輸時所受大氣騷動的影響要比完全相干光小得多;並且部分相干光束具有光強比較均勻，對散斑低靈敏等優點而被應用於激光核聚變等領域。 -
( 5 ) analysis of data measured with multi - element regression, and optimized mathematics model of grain moisture measurement is brought forward based on contrast of several stat parameters. the particular operating of data fusion method based on parameter estimation is used. the validation is proved by increasing the measurement precision and reducing the ucertain factor
（ 5 ）採用多元回歸分析的方法，對檢測數據進行了分析，在運用各種統計參數進行比較分析的基礎上提出了糧食水分檢測的最佳數學模型。分析了採用基於參數估計方法進行數據融合的基本原理，驗證了此方法對于減小不確定因素影響，提高檢測精度的作用。 -
Firstly, influence factors of generalization of neural network are presented in this thesis, in order to improve neural network ’ s generalization ability and dynamic knowledge acquirement adaptive ability, a structure auto - adaptive neural network new model based on genetic algorithm is proposed to optimize structure parameter of nn including hidden layer nodes, training epochs, initial weights, and so on ; secondly, through establishing integrating neural network and introducing data fusion technique, the integrality and precision of acquired knowledge is greatly improved. then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement, knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system, by completing missing value data and eliminating unnecessary attributes, discretization of continuous attribute, reducing redundancy, extracting rules in this thesis. finally, rough sets theory and neural network are combined to form rnn ( rough neural network ) model for acquiring knowledge, in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement, and rnn can improve the speed and quality of knowledge acquirement greatly
本文首先討論了影響神經網路的泛化能力的因素，提出了一種新的結構自適應神經網路學習演算法，在新方法中，採用了遺傳演算法對神經網路的結構參數（隱層節點數、訓練精度、初始權值）進行優化，大大提高了神經網路的泛化能力和知識動態獲取自適應能力;其次，構造集成神經網路，引入數據融合演算法，實現了基於集成神經網路的融合診斷，有效地提高了知識獲取的全面性、完善性及精度;然後，針對知識獲取過程中所存在的不確定性、不完備性等問題，探討了運用粗糙集理論的知識獲取方法，通過缺損數據補齊、連續數據的離散、沖突消除、冗餘信息約簡、知識規則抽取等一系列的演算法實現了智能診斷的知識規則獲取;最後，將粗糙集理論與神經網路相結合，研究了粗糙集-神經網路的知識獲取方法。
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