利用模糊類神經網路模型進行學習成效預估

林家聖

doi:10.6840/cycu200500047

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利用模糊類神經網路模型進行學習成效預估

Estimation of Learning Effectiveness by Fuzzy Neural Network Models

林家聖 , Masters　　Advisor：賀嘉生　　

繁體中文 DOI： 10.6840/cycu200500047

資料預測；模糊理論；資料探索；類神經網路；時序型資料； fuzzy theory ； data serial ； neural network ； data mining ； estimation

Abstract │ Reference (39) │ Times Cited (1) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

Research in data mining domain have a long period, many researcher works hard for digging more and useful information, which are some kind of relationship or rules.  But with these reslationship and rules, they tell people what happened in and if there are some behavior patterns in the past.  Besides, these patterns not only being patterns but also tell people where the future is.  These patterns bring the age of research in estimation, but most of these researches pay too much attention on business activities, such as stock trend, demand forcasting etc.  Some researchs in non-business activities are exist surely, for example the predition of climate or cache which applied to internet.  But only few researches focused on education.
In this thesis, we have designed 3 kinds of data estimating model, with the data clustering theory in data mining domain, by using the self-organizing map (SOM) of neural network.  These models are state-transition matrix, state-transition probabilitic model and continuous state-transistion model, which use in serial data for finding internal pattern transition.  By using of these these models, it gets some possible value in the future, and these values will be extracted for the inner information by fuzzy theory.  With the information, we can find the probabilities of some default values, for a formal report of estimation.
This theis brings up not only a estimation method, but also an full-loop data mining process.  The process integrates with the estimation method which has mentioned above, it applies to the the estimation of the l nmg effectiveness.  Full-loop data mining process is like most KDD processes, from original data to model, but the process also uses these models for analyzing the grades to students for a feedback.  Experiments in the thesis predict the values of fall of students by using 1 to 3 models, to confirm if the estimation more accurate by the cooperation of more models.  Beside, for confirming the generality, we also used the data of student grades in different college.

Reference ( 39 ) 〈TOP〉

[黃天鴻03] 黃天鴻，「以知識地圖為基礎發展的網路全迴路學習」，中原大學資訊工程研究所碩士論文，2003
連結：
[Aok79] Takashi Aoki, “A statistical prediction of the tropical cyclone position based on persistence and climatological factor in the western North Pacific (the PC method),” Geophys. Mag., Japan Meteoro-Logical Agency, No.4. 1979
連結：
[BaP92] H.-U. Bauer and K. Pawelzik, “Quantifying the neighborhood preservation of self-organizing feature maps”, IEEE Trans. Neural Networks, Vol.3, No.4, 1992, pp-570-579.
連結：
[BaW03] Arvind K. Bansal and Cristopher J. Woolverton, “Applying Automatically Derived Gene-Group to Automatically Predict and Refine Metabolic Pathways,” IEEE Transactions on Knowledge and Data Engineering, 2003, pp.883-894.
連結：
[BeZ70] R.A. Bellman and L.A. Zadeh, “Decision-making in a fuzzy environment,” Management Sciences 17, 1970, pp.141-164.
連結：

Times Cited (1) 〈TOP〉

呂理仲(2008)。12導程心電圖快速擷取技術研發。臺北科技大學機電整合研究所學位論文。2008。1-72。

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