連續性學習論 的英文怎麼說

中文拼音 [liánxùxìngxuéxílún]
連續性學習論 英文
continuity theory of learning
  • 連 : Ⅰ動詞1 （連接） link; join; connect 2 （連累） involve （in trouble）; implicate 3 [方言] （縫） ...
  • 續 : Ⅰ形容詞（連接不斷） continuous; successive Ⅱ動詞1 （接在原有的后頭） continue; extend; join 2 （...
  • 性 : Ⅰ名詞1 （性格） nature; character; disposition 2 （性能; 性質） property; quality 3 （性別） sex ...
  • 學 : Ⅰ動詞1 （學習） study; learn 2 （模仿） imitate; mimic Ⅱ名詞1 （學問） learning; knowledge 2 （學...
  • 論 : 論名詞（記錄孔子及其門徒的言行的「論語」） the analects of confucius
  • 連續 : continuation; succession; series; continuity; continuing; running; continuous; successive; contin...
  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. Based on continuous time system, convergence discussion and testifying were made to iterative learning control algorithm under the condition of constraints. then algorithm a and algorithm b that mentioned before are testified that they can be used under the conditions of that controller output has constraints

    本文針對這一情況作了討論，基於連續時間系統，對控制器輸出有限制的情況下的迭代學習演算法做了收斂性討論和證明，並且證明了前面提出的演算法a和演算法b可用於控制器輸出有限制情況下的機械手控制。
  3. Based on other research, the author pays his main attention to the feature of associative memory for chaotic neural network and researches ' on the following three aspects : i ) research on the parameter effecting on associative memory properties. ii ) research on the structure effecting on many - to - many associative memory properties. iii ) research on the successive learning ability

    在他人研究基礎上，本論文深入探討了混沌神經網路的聯想記憶特性，著重研究了以下三個方面： （ 1 ）網路參數對聯想記憶特性的影響； （ 2 ）網路結構對多對多聯想的影響； （ 3 ）混沌神經網路的連續學習能力。
  4. An information entropy - based uncertainty measure is presented first based on generalized rough set model in this paper, which is suitable for evaluating rules retrieved from noisy data. second, this paper puts forward generalized minimal - and - maximal - rules - learning methods and generalized maximal - minimal - rules - conversion model because we can encounter noisy problems in most real - life problems. third, this paper puts forward a new discretization method for the continuous attributes, which is based on the clustering and rough sets theory

    本文在對粗集及其相關理論的研究基礎上，首先給出了一種基於推廣粗集模型和信息熵的規則不確定性量度，該不確定性量度適于評價從有噪音數據中提取的規則；鑒于實際應用中經常能遇到噪音的問題，本文提出廣義極小極大規則學習方法，同時還提出了廣義極大極小規則轉換模型gmm ；最後，本文基於聚類方法、結合粗集理論提出了一種新的連續屬性離散化方法。
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