冗餘運算消除 的英文怎麼說
中文拼音 [rǒngyúyùnsuànxiāochú]
冗餘運算消除
英文
redundant operation elimination- 冗 : [書面語]Ⅰ形容詞1. （多餘的） superfluous; redundant 2. （煩瑣） full of trivial detailsⅡ名詞（繁忙的事） business
- 餘 : Ⅰ同「余」Ⅰ-Ⅳ1. Ⅱ名詞（姓氏） a surname
- 運 : Ⅰ動詞1 （物體位置不斷變化） move; revolve 2 （搬運; 運輸） carry; transport 3 （運用） use; wield...
- 算 : Ⅰ動詞1 （計算數目） calculate; reckon; compute; figure 2 （計算進去） include; count 3 （謀劃;計...
- 消 : 動詞1 （消失） disappear; vanish 2 （使消失; 消除） eliminate; dispel; remove 3 （度過; 消遣） pa...
- 除 : Ⅰ動詞1 （去掉） get rid of; eliminate; remove 2 [數學] （用一個數把另一個數分成若干等份） divide:...
- 冗餘 : redundance; redundancy冗餘校驗 redundancy check; redundant check; 冗餘碼 redundant code; redundan...
- 運算 : [數學] operation; arithmetic; operating
- 消除 : eliminate; dispel; remove; clear up; wipe off
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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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