【摘要】：自1995年以來(lái)，，知識(shí)融合作為一個(gè)獨(dú)立的研究領(lǐng)域，得到了長(zhǎng)足的發(fā)展。雖然在基本理論構(gòu)建方面尚不完善，但其在應(yīng)用領(lǐng)域內(nèi)的突出表現(xiàn)充分體現(xiàn)了知識(shí)融合的價(jià)值所在。在眾多領(lǐng)域內(nèi)的研究，已經(jīng)證明了知識(shí)融合能夠更好的幫助用戶(hù)解決問(wèn)題。因此，研究借助知識(shí)融合的手段對(duì)制造企業(yè)的創(chuàng)新績(jī)效進(jìn)行評(píng)價(jià)。使企業(yè)能夠更好的了解當(dāng)前創(chuàng)新?tīng)顩r，提升自身創(chuàng)新績(jī)效。 本研究采用粗糙集理論和知識(shí)融合方法對(duì)制造企業(yè)創(chuàng)新績(jī)效影響因素進(jìn)行了研究。通過(guò)研究國(guó)內(nèi)外創(chuàng)新績(jī)效影響因素相關(guān)文獻(xiàn)，并結(jié)合數(shù)據(jù)的可獲得性，選取了創(chuàng)新績(jī)效評(píng)價(jià)指標(biāo)。參考近年來(lái)學(xué)者提出的利用粗糙集構(gòu)建多知識(shí)庫(kù)的初步想法，采用了模糊C均值聚類(lèi)算法完成了連續(xù)屬性的離散化，借助遺傳算法對(duì)制造企業(yè)的創(chuàng)新績(jī)效評(píng)價(jià)指標(biāo)進(jìn)行了屬性約簡(jiǎn)，并引入粗糙集理論中的屬性依賴(lài)度和屬性重要度的概念，構(gòu)建了多組企業(yè)創(chuàng)新績(jī)效評(píng)價(jià)模型。再將評(píng)價(jià)模型與對(duì)應(yīng)約簡(jiǎn)所形成的規(guī)則庫(kù)相結(jié)合，共同組成了用于知識(shí)融合的知識(shí)源。然后，采用模糊積分的方法對(duì)多組創(chuàng)新績(jī)效評(píng)價(jià)模型進(jìn)行了融合，并建立了包括請(qǐng)求處理模塊、信息搜集模塊、多知識(shí)源構(gòu)建模塊、知識(shí)融合模塊、結(jié)果反饋模塊等主要功能模塊在內(nèi)的創(chuàng)新績(jī)效最優(yōu)決策融合模型。完成了服務(wù)于制造企業(yè)創(chuàng)新績(jī)效評(píng)價(jià)的知識(shí)融合模型的相關(guān)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(duì)創(chuàng)新績(jī)效最優(yōu)決策融合模型進(jìn)行了應(yīng)用研究。研究結(jié)果表明經(jīng)過(guò)知識(shí)融合的評(píng)價(jià)結(jié)果能夠更加準(zhǔn)確的判斷企業(yè)的創(chuàng)新績(jī)效。同時(shí)，還發(fā)現(xiàn)了企業(yè)規(guī)模和知識(shí)積累對(duì)創(chuàng)新績(jī)效的影響最大，其中企業(yè)規(guī)模對(duì)創(chuàng)新績(jī)效具有正向推動(dòng)作用，而知識(shí)積累則與創(chuàng)新績(jī)效呈現(xiàn)負(fù)相關(guān)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(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F425;F273.1;F224

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