近年來，房地產之投資及買賣廣為盛行，而房地產依舊為人們投資的方向之一。屬於人工智慧範疇之類神經網路，其具有學習能力，可以進一步的歸納推演所要預估的結果，也適合應用於非線性的問題中，但以往類神經網路的機器學習模型，皆採用中央處理器(CPU)進行運算，在計算量龐大時往往耗費大量時間於訓練上。而圖形處理器(GPU)之崛起，將增進機器學習的速率。 本研究利用穩健學習程序搭配信封模組的概念，建立一類神經網路系統，利用GPU設備及機器學習工具–Tensorflow實作，針對民國一零四年之台北市不動產交易之住宅資料，並使用1276筆資料，隨機選取60%資料作為訓練範例並分別進行以假設有5%為可能離群值及沒有之情況做學習，並選取影響房地產價格之11個變數做為輸入變數，對網路進行訓練，實證結果發現類神經網路的速度有顯著的提升；且在假定有5%離群值之狀況下學習有較好的預測水準；另外在對資料依價格進行分組後，顯示此網路在對中價位以上的資料有較好的預測能力。就實務應用方面，藉由類神經網路適合應用於非線性問題的特性，對未來房地產之估價系統輔助做為參考。 / Real estate investment and transcation prevails in recent year. And it is still one of the choices for people to invest. The Neural Network which belongs to the category of Arificial Intelligence has the ability to learn and it can deduce to reach the goal. In addi-tion, it is also suitable for the application of non-linear problems. However, the machine learning model of the Neural Network use CPU to operate before and it will always spend a lot of time on training when the calculation is large.However, the rise of GPU speeds up the machine learing system. This study will implement resistant learning procedure with the concept of Enve-lope Bulk focus to built a Neural Network system. Using TensorFlow and graphics pro-cessing unit (GPU) to speed up the original Arificial Intelligence system. According to the real estate transaction data of Taipei City in 2015, 1276 data will be used. We will pick 60% of the data in a random way as training data of our two experiment , one of it will assume that there are 5% of outlier and another won’t. Then select 11 variables which may impact the value of real estate as input. As the experiment result, it makes the operation more efficient and faster , training of the Neural Network really speed up a lot. The experiment which has assume that there are 5% of outlier shows the better effect of predicting than the another. And we got a better prediction on the part of the higher price after we divided the data into six groups by their price.In the other hand, Neural Network is good at solving the problem of non-linear. It can be a reference of the sup-port system of real estate appraisal in the future.

雲端的盛行使得人們做任何事都要透過網路，但是總會有些有心人士使用一些惡意程式來創造攻擊或通過網絡連接竊取資料。為了防止這些網路惡意攻擊，我們必須不斷檢查網路流量資料，然而現在這個雲端時代，網路的資料是非常龐大且複雜，若要檢查所有網路資料不僅耗時而且非常沒有效率。 本研究使用TensorFlow與多個圖形處理器(Graphics Processing Unit, GPU)來實作類神經網路(Artificial Neural Networks, ANN)機制，用以分析網路流量資料，並得到一個可以判斷正常與異常網路流量的偵測規則，也設計一個實驗來驗證我們提出的類神經網路機制是否符合網路流向異常偵測的時效性和有效性。 在實驗過程中，我們發現使用更多的GPU可以減少訓練類神經網路的時間，並且在我們的實驗設計中使用三個GPU進行運算可以達到網路流量異常偵測的時效性。透過該方法得到的初步實驗結果，我們提出機制的結果優於使用反向傳播算法訓練類神經網路得到的結果。 / The prosperity of the cloud makes people do anything through the Internet, but there are people with bad intention to use some malicious programs to create attacks or steal information through the network connection. In order to prevent these cyber-attacks, we have to keep checking the network traffic information. However, in the current cloud environment, the network information is huge and complex that to check all the information is not only time-consuming but also inefficient. This study uses TensorFlow with multiple Graphic Processing Units (GPUs) to implement an Artificial Neural Networks (ANN) mechanism to analyze network traffic data and derive detection rules that can identify normal and malicious traffics, and we call it Network Traffic Anomaly Detection (NTAD). Experiments are also designed to verify the timeliness and effectiveness of the derived ANN mechanism. During the experiment, we found that using more GPUs can reduce training time, and using three GPUs to do the operation can meet the timeliness in NTAD. As a result of this method, the experiment result was better than ANN with back propagation mechanism.

本系統係藉由分析歷史交易資料來預測S&P500期貨市場之漲幅。 我們改進了Tsaih et al. (1998)提出的混和式AI系統。 該系統結合了Rule Base 系統以及類神經網路作為其預測之機制。我們針對該系統在以下幾點進行改善：(1) 將原本的日期資料改為使用分鐘資料作為輸入。(2) 本研究採用了“移動視窗”的技術，在移動視窗的概念下，每一個視窗我們希望能夠在60分鐘內訓練完成。(3)在擴增了額外的變數 – VIX價格做為系統的輸入。(4) 由於運算量上升，因此本研究利用TensorFlow 以及GPU運算來改進系統之運作效能。 我們發現VIX變數確實可以改善系統之預測精準度，但訓練的時間雖然平均低於60分鐘，但仍有部分視窗的時間會小幅超過60分鐘。 / The system is made to predict the Futures’ trend through analyzing the transaction data in the past, and gives advices to the investors who are hesitating to make decisions. We improved the system proposed by Tsaih et al. (1998), which was called hybrid AI system. It was combined with rule-based system and artificial neural network system, which can give suggestions depends on the past data. We improved the hybrid system with the following aspects: (1) The index data are changed from daily-based in into the minute-based in this study. (2) The “moving-window” mechanism is adopted in this study. For each window, we hope we can finish training in 60 minutes. (3) There is one extra variable VIX, which is calculated by the VIX in this study. (4) Due to the more computation demand, TensorFlow and GPU computing is applied in our system. We discover that the VIX can obviously has positively influence of the predicting performance of our proposed system. The average training time is lower than 60 minutes, however, some of the windows still cost more than 60 minutes to train.