【摘要】：自1995年以來，，知識融合作為一個獨立的研究領域，得到了長足的發(fā)展。雖然在基本理論構建方面尚不完善，但其在應用領域內的突出表現充分體現了知識融合的價值所在。在眾多領域內的研究，已經證明了知識融合能夠更好的幫助用戶解決問題。因此，研究借助知識融合的手段對制造企業(yè)的創(chuàng)新績效進行評價。使企業(yè)能夠更好的了解當前創(chuàng)新狀況，提升自身創(chuàng)新績效。 本研究采用粗糙集理論和知識融合方法對制造企業(yè)創(chuàng)新績效影響因素進行了研究。通過研究國內外創(chuàng)新績效影響因素相關文獻，并結合數據的可獲得性，選取了創(chuàng)新績效評價指標。參考近年來學者提出的利用粗糙集構建多知識庫的初步想法，采用了模糊C均值聚類算法完成了連續(xù)屬性的離散化，借助遺傳算法對制造企業(yè)的創(chuàng)新績效評價指標進行了屬性約簡，并引入粗糙集理論中的屬性依賴度和屬性重要度的概念，構建了多組企業(yè)創(chuàng)新績效評價模型。再將評價模型與對應約簡所形成的規(guī)則庫相結合，共同組成了用于知識融合的知識源。然后，采用模糊積分的方法對多組創(chuàng)新績效評價模型進行了融合，并建立了包括請求處理模塊、信息搜集模塊、多知識源構建模塊、知識融合模塊、結果反饋模塊等主要功能模塊在內的創(chuàng)新績效最優(yōu)決策融合模型。完成了服務于制造企業(yè)創(chuàng)新績效評價的知識融合模型的相關研究工作。 最后，選取了159家在中小企業(yè)板上市的制造企業(yè)作為樣本，對創(chuàng)新績效最優(yōu)決策融合模型進行了應用研究。研究結果表明經過知識融合的評價結果能夠更加準確的判斷企業(yè)的創(chuàng)新績效。同時，還發(fā)現了企業(yè)規(guī)模和知識積累對創(chuàng)新績效的影響最大，其中企業(yè)規(guī)模對創(chuàng)新績效具有正向推動作用，而知識積累則與創(chuàng)新績效呈現負相關性。
[Abstract]:Since 1995, as an independent research field, knowledge fusion has made great progress. Although the construction of basic theory is not perfect, its outstanding performance in the field of application fully embodies the value of knowledge fusion. Research in many fields has proved that knowledge fusion can better help users solve problems. Therefore, the paper evaluates the innovation performance of manufacturing enterprises by means of knowledge fusion. So that enterprises can better understand the current state of innovation, improve their own innovation performance. In this study, rough set theory and knowledge fusion method are used to study the factors affecting innovation performance of manufacturing enterprises. By studying the related literature of influencing factors of innovation performance at home and abroad, and combining with the availability of data, the evaluation index of innovation performance is selected. Referring to the preliminary idea of using rough set to construct multi-knowledge base, the fuzzy C-means clustering algorithm is used to discretize the continuous attributes. With the help of genetic algorithm, the attribute reduction of innovation performance evaluation index of manufacturing enterprises is carried out, and the concepts of attribute dependency degree and attribute importance degree in rough set theory are introduced, and a multi-group innovation performance evaluation model is constructed. Then the evaluation model is combined with the rule base formed by the corresponding reduction to form a knowledge source for knowledge fusion. Then, the fuzzy integral method is used to fuse the multi-group innovation performance evaluation model, which includes the request processing module, the information collection module, the multi-knowledge source construction module, the knowledge fusion module. Results the optimal decision fusion model of innovation performance including the main functional modules such as feedback module. The related research work of knowledge fusion model serving for the evaluation of innovation performance of manufacturing enterprises is completed. Finally, 159 manufacturing enterprises listed on SME board are selected as samples to study the optimal decision fusion model of innovation performance. The results show that the evaluation results of knowledge fusion can judge the innovation performance more accurately. At the same time, it is found that enterprise size and knowledge accumulation have the greatest impact on innovation performance, in which enterprise scale has a positive role in promoting innovation performance, while knowledge accumulation has a negative correlation with innovation performance.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F425;F273.1;F224

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