節理糙度數值 的英文怎麼說
中文拼音 [jiélǐcāodùshǔzhí]
節理糙度數值
英文
joint roughness value- 節 : 節構詞成分。
- 理 : Ⅰ名詞1 （物質組織的條紋） texture; grain （in wood skin etc ） 2 （道理;事理） reason; logic; tru...
- 糙 : 形容詞（粗糙; 不細致） rough; coarse; crude
- 度 : 度動詞[書面語] （推測; 估計） surmise; estimate
- 數 : 數副詞（屢次） frequently; repeatedly
- 數值 : numerical value; numerial number; figure; magnitude; value數值表 numerical tabular; 數值天氣預報 ...
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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。Pattern recognition and fault diagnosis based on the rough sets theory and neural networks is studied in this dissertation. rough set theory in the noise environment and in the real region is generalized, and as the sametime, the methods of combine rough set theory with neural networks are proposed. the main contents of the dissertation are organized as follow : at first, a relation of nearness instead of indiscernibility is proposed for increasing the robustness of decision system which consists of noise pollution data
論文運用粗糙集理論與神經網路方法進行了模式識別和故障診斷方面的研究，對在噪聲下和實數領域的粗糙集模型進行了擴展，研究了粗糙集與神經網路的多種集成應用方法，全文的主要內容如下:首先，論文針對經典粗糙集理論中的不可分辨關系對連續屬性值中噪聲數據缺乏容錯性的情況，提出一種相近關系代替不可分辨關系，並用不同的值調節相近關系中可接受的相近程度，限制了可冗餘的范圍。
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