散列存取法 的英文怎麼說

中文拼音 [sǎnliècúnqǔfǎ]
散列存取法 英文
hashing access method
  • 散 : 散動詞1. （由聚集而分離） break up; disperse 2. （散布） distribute; disseminate; give out 3. （排除） dispel; let out
  • 列 : Ⅰ動1 （排列） arrange; form a line; line up 2 （安排到某類事物之中） list; enter in a list Ⅱ名詞1...
  • 存 : 動詞1 （存在; 生存） exist; live; survive 2 （儲存; 保存） store; keep 3 （蓄積; 聚集） accumulat...
  • 取 : Ⅰ動詞1 （拿到身邊） take; get; fetch 2 （得到; 招致） aim at; seek 3 （採取; 選取） adopt; assume...
  • 法 : Ⅰ名詞1 （由國家制定或認可的行為規則的總稱） law 2 （方法; 方式） way; method; mode; means 3 （標...
  1. 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。
  2. After january, 2003, “ the law of government procurement ” came into force, both concentrative purchase and dispersive purchase are use by government, which starts an new trend of government purchase progress, using concentrative purchase to do more large quantity purchase, dispersive purchase is used less and less, it only used to do little quantity purchase. the list of government purchase has become longer and longer, the variety of materials and the quantity of materials has increased so sharply. our country ’ s government purchase has made a great progress, but but we still need to take notice of the other side of the fact, there are two important issues, the first is the obstacle of the reforming process in the field of public finance system ; and the second is the kinds of problems occurred in the process of government procurement

    政府采購制度改革是我國公共財政改革的重要組成部分，引入我國從試點到推廣實施僅有十年的時間，卻取得了巨大的進展， 《政府采購法》在2003年1月開始實施后，我國採用集中采購與分散采購相結合的政府采購方式，開始了我國政府采購的集中化趨勢進程，從早期分散采購演變為大批量的集中的采購方式，當然，小批量分散采購也結合其中，列入政府集中采購目錄的物資種類與采購數量大大增加，取得了巨大的進展，卻也存在著不少的問題，具體表現為政府采購過程中出現的各種操作性問題和政府采購制度推進的障礙，以及政府采購的環境效益問題，操作性問題和政府采購制度推進的障礙問題本質上可歸納為兩方面，即內在因素，這是事前性的，偏重於制度性、體制性等根本問題;外在因素，這是管理操作層面的因素，政府采購的環境效益問題是由政府是否采購綠色產品，推動綠色產品在社會的使用而引出的，於是，對政府采購集中化趨勢中所暴露出的問題進行分析，找出問題的根源以探尋解決的思路及對策就顯得尤為急迫。
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