rules of generalization 中文意思是什麼

rules of generalization 解釋
廣義化規則
  • rules : 安全法規
  • of : OF ＝Old French 古法語。
  • generalization : n. 1. 一般化，普遍化。2. 概括，綜合，總結；歸納；法則化。3. 廣義；概說；概念，通則。
  1. The main subjects of this paper are as follows : ( 1 ) the conditions of map use, characters of map regions and map scale are considered during map generalization. we also build several tools to dynamic sum up and pack up generalization rules and parameters and put them to generalization knowledge library

    考慮了影響制圖綜合的三要素，即地圖用途、制圖區域地理特點和地圖比例尺，並實現了對綜合規則、參數的歸納和整理，建成了可動態擴展的規則庫和參數庫。
  2. Their learning and training rules have been analyzed profoundly and their abilities to approximate arbitrary nonlinear function have been testified and compared by the simulation. a new rbf neural network has been presented which uses a raised - cosine function as activation transfer function. it provides a wider generalization in comparison with gaussian rbf neural networks by simulation as well as strong approximation ability, fast convergence, a rule to select the parameters of the networks

    本文詳細研究了兩種典型的前向神經網路（ bp網路和rbf網路）的學習和訓練演算法，提出了一種新穎的基於緊支集餘弦函數的徑向基神經網路，其克服了常用的高斯型rbf神經網路雖具有緊支集但各基函數非正交的不足，其收斂速度快、網路參數選取有理論依據且相比于高斯型rbf神經網路具有更強的泛化能力，模擬驗證了其有效性。
  3. 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。
  4. To overcome the difficulty in determining the rbf center numbers and spread, a kind of generalized genetic algorithm is introduced, which follows the analysis of the basic rules of genetic algorithm. the new hybrid algorithm determines the center numbers and spread adaptively to reach the optimal tradeoff between the training accuracy and the generalization, so it increases the prediction accuracy of the model

    針對建模過程中出現的rbf中心和寬度難以確定的難點，在分析遺傳演算法機理和基本演算法的基礎上，提出了使用廣義遺傳演算法對rbf神經網路模型的中心和寬度進行自適應選擇，從而達到模型訓練精度和范化能力的一個最優的平衡，從而提高模型的預報精度。
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