【摘要】：模糊神經(jīng)網(wǎng)絡是人工神經(jīng)網(wǎng)絡領域的一個重要分支,并且廣泛的應用于系統(tǒng)控制、建模等問題當中,并取得了不俗的成就。不過面對維度高、關聯(lián)性低的數(shù)據(jù)時,因為某個結(jié)果只和數(shù)據(jù)中的一項或幾項相關,所以模型的預測結(jié)果差,生成過多的模糊規(guī)則也不易于被人理解。就存在的一些相應問題,本文對其進行相應討論與研究,包括:輸入向量的選取方式、模糊神經(jīng)網(wǎng)絡的學習能力、模糊集合的劃分、模糊規(guī)則的生成方式及誤差等問題。本論文的工作內(nèi)容包括:1、研究了國內(nèi)外模糊神經(jīng)網(wǎng)絡的現(xiàn)狀,研究了國內(nèi)外教育數(shù)據(jù)的挖掘技術,并指出了理論與應用中存在的一些問題。2、針對存在的問題,比較了多種模型,在自適應模糊神經(jīng)推理系統(tǒng)(Adaptive Network-based Fuzzy Inference System)的基礎之上,對模糊神經(jīng)網(wǎng)絡模型進行了適當?shù)母倪M,使其更適用于高維度、低關聯(lián)的數(shù)據(jù),可以找出數(shù)據(jù)中關聯(lián)性較大若干數(shù)據(jù)項,描述其關系,并生成易于人理解的知識。3、使用修改后的自適應神經(jīng)模糊推理系統(tǒng),進行計算機驗證,生成若干輸入變量,其中一部分與輸出變量有關,一部分與輸出變量無關,尋找出與輸出變量有關的輸入量并做出相應解釋。4、將模型應用在大學生的教育數(shù)據(jù)上,根據(jù)各科成績的相關性,做出相應的成績預測。以某些先導課程的成績來預測學生將來某些科目的成績,解決了傳統(tǒng)神經(jīng)網(wǎng)絡,在高維度輸入時,訓練時間過長,預測結(jié)果較差的弊端。新的方法能夠?qū)ふ腋鞣N的科目成績之間的關系,并做出了相應解釋,預測的精度更高,誤差更小,而且有較強的解釋性。
[Abstract]:The fuzzy neural network is an important branch in the field of artificial neural network, and it is widely used in the problems of system control and modeling. However, in the case of data with high dimension and low correlation, because a result is only related to one or several of the data, the prediction result of the model is poor, and the generation of too many fuzzy rules is not easy to be understood. In this paper, some corresponding problems are discussed, including the selection of input vector, the learning ability of the fuzzy neural network, the division of the fuzzy set, the generation of the fuzzy rule and the error. The working contents of this paper are as follows:1. The present situation of the fuzzy neural network at home and abroad is studied, the mining technology of the domestic and foreign educational data is studied, and some problems in the theory and application are pointed out. Based on the Adaptive Network-based Fuzzy Inference System, the fuzzy neural network model is modified appropriately, so that it is more suitable for high-dimension and low-associated data, which can find a number of data items in the data and describe the relation. a modified adaptive neural fuzzy inference system is used for computer verification to generate a plurality of input variables, a part of which is related to an output variable, a part of which is independent of the output variable, And finding out the input quantity related to the output variable and making corresponding explanation.4, applying the model to the education data of the college students, and making corresponding achievement prediction according to the correlation of the results of the subjects. The results of some pilot courses are used to predict the performance of some subjects in the future, and the traditional neural network is solved. In the case of high-dimension input, the training time is too long and the prediction result is poor. The new method can find the relation between the subject's achievements and make a corresponding explanation. The accuracy of the prediction is higher, the error is smaller, and there is a strong explanation.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP183

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