規避性學習 的英文怎麼說

中文拼音 [guībìxìngxuéxí]
規避性學習 英文
avoiding learning
  • 規 : Ⅰ名詞1 （畫圓形的工具） instrument for drawing circles 2 （規則; 成例） rule; regulation 3 [機械...
  • 避 : 動詞1. （躲開;迴避） avoid; evade; dodge; seek shelter; shun; flee 2. （防止; 避免） prevent; keep away; repel
  • 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...
  • 規避 : （設法躲避） evade; dodge; avoid; set around; escapism
  1. In rsdm, binary patterns are replaced by real - valued patterns, accordingly avoiding the coding process ; the outer learning rule is replaced by regression rule, therefore the model has not only the ability of pattern recognition but the ability of function approximation. the prearrangement of the address array bases on the distribution of patterns. if the distribution of patterns is uniform. then the address array is prearranged randomly, otherwise predisposed with the theory of genetic algorithm and the pruneing measure so as to indicate the distribution of patterns and improve the network performance. non - linear function approximation, time - series prediction and handwritten numeral recognition show that the modified model is effective and feasible

    在rsdm中，以實值模式代替二值模式，避免了實值到二值的編碼過程：以回歸學習規則代替外積法，使該模型在具有識別能力的同時具有了對函數的逼近能力；地址矩陣的預置根據樣本的分佈採取不同方法，若樣本均勻分佈，則隨機預置，否則利用遺傳演算法的原理和消減措施來預置地址矩陣，使之反映樣本的分佈，改善網路的性能。
  2. In this dissertation, two kinds of optimization, methods are proposed. firstly, only these linking weights corresponding to the control rules that affect the control performance significantly are updated in order to reduce the compute works and speed up the training progress. secondly, the updating step is adjusted adaptively in accordance with the error and the change of error of the system based on the t - s model to get better performance

    針對模糊神經網路控制器一般存在著在線權值調整計算量大、訓練時間長、過度修正權值可能導致系統劇烈振蕩等缺點，提出了兩種模糊神經網路控制器的優化方法：在線自學習過程中僅對控制性能影響大的控制規則相關的權值進行修正，以減小計算量，加快訓練速度；基於t - s模糊模型，根據偏差及偏差變化率大小動態自適應調節權值修正步長，抑制控制器輸出的劇烈變化，避免系統發生劇烈振蕩。
  3. Avoiding the use of mathematic models, the fuzzy logical control ( flc ) system meets the control demands of the tunnel ventilation process well which is a nonlinear distribution system. but because of its lacking of learning and adaptive ability, many problems has emerged when using flc : the membership functions of the fuzzy variables cannot be changed, the fuzzy logical rules cannot be modified automatically when environmental variables such as traffic model, average exhaust, etc, are changed. for this reason, the paper uses fuzzy neural network control ( fnnc ) system to improve the control process

    模糊控制系統避開了數學模型，能很好適應公路隧道通風系統非線性和分散式參數特徵，但是模糊控制系統本身的學習和適應能力差，導致了模糊變量各語言值隸屬函數和控制規則不能隨著環境參數（如交通量、基準排放量等）的改變而自動調整和修改等問題，本文將具有強大學習能力的神經網路融合到模糊控制系統中，研究和探討了隧道通風模糊神經控制方法。
  4. In a classroom composed of children from weak, uncertainty avoidance cultures we might expect to see students feeling comfortable in unstructured learning situations and students also being rewarded for innovative approaches to problem solving

    如果課堂上的小孩子兒童來源來自於低度不確定性規避文化，那麼將他們置於權利的學習條件水平下並獎賞那些發明性處理解決搞定疑問和問題的學生，這種方式會讓他們感到比較對比思考分析與判斷舒服。
  5. Svm is a kind of universal learning algorithm, which developed from statistical learning theory, that is, small sample learning theory proposed by vapnik. it can represent complicated functions especially in high dimensions, which can avoid the trouble of the dimension tragedy that happened in general algorithm. it also will not affect the system performance by using original data of the array for the eigenvector directly

    支持向量機是由vapnik等人提出的小樣本統計理論? ?統計學習理論發展而來的一種新的通用學習演算法，特別在高維空間中表示復雜函數，避免了常規演算法「維數災難」等麻煩問題，可直接用陣列原始數據作為特徵向量而不影響系統性能。
  6. The control policy of the behaviors such as avoiding and pursuing is the combination of potential field method and behavior fusion method. the reinforcement learning based on existing knowledge is used to modify the importance parameters of each behavior, which will avoid the disadvantage of rules

    在對行為層規劃的研究中，運用基於勢場法思想的行為融合方法實現多機器人編隊的避碰和追蹤策略，並用嵌入先驗知識的強化學習方法對融合過程中的重要性參數進行調整，彌補了基於規則設計的不完整性。
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