initial attribute 中文意思是什麼
initial attribute
解釋
初始屬性-
Through almost a hundred years of development, the study of accounting, especially the measurement methods in financial reporting, has gone through a series of advancements, from the initial application of historical cost, to replacement cost, net realizable value, present value, until in september 2006, the financial accounting standards board published fas 157 ? fair value measurements, and made it into the world recognized fifth accounting measurement attribute
會計學經過近百年的發展演進，企業財務評價與信息披露的計量方法，由最初的歷史成本計量屬性逐步發展到重置成本、可變現凈值、現值，一直到2006年9月美國財務會計準則委員會發布了美國財務會計準則第157號? ?公允價值計量，使其成為第五種會計計量屬性，並且得到了世界各國的共同認可。 -
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。
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