近年來生成對抗網路（Generative Adversarial Networks, GANs）被提出用以
生成模擬圖片，且已有部分文獻將GAN 應用於數值資料之分析，如建築能源消
耗的預測以及肝癌期數的預測與辨別。然此些研究均以一般資料量或大數據資料
量而為之，而在現今全球化的時代，快速決策之需求漸增，但囿於短時間內可獲
得的資料稀少，導致機器學習演算法難以產出穩健的結果，如何從少數樣本中獲取更多訊息已成為一項課題。因此，如何發展適用於小樣本資料分析之GAN 架構，實為本研究之目的。本研究將以Wasserstein GAN（WGAN）做為本研究之GAN 架構，並改良整體趨勢擴散（mega-trend-diffusion, MTD）作為GAN 之生成網路限制，以倒傳遞神經網路（back-propagation network, BPN）作為GAN 之生成網路與鑑別網路，稱為WGAN_MTD。本研究透過此改良後之GAN 架構，使小樣本資料也能透過GAN生成出具有相似於真實樣本之虛擬樣本。我們從UC Irvine Machine Learning Repository 中挑選了兩筆公開資料集並透過多個分類模式與未增生虛擬樣本之方式比較準確度等測量指標。本研究對比未生成虛擬樣本的情況下，提供更好或接近最佳的結果。

In recent years, Generative Adversarial Networks (GANs) have been proposed to generate simulated images, and some literatures have applied GAN to the analysis of numerical data, such as the prediction of building energy consumption and the
prediction and identification of liver cancer stages. However, these studies are based on general data volume or big data volume. In the current era of globalization, the demand for rapid decision-making is increasing, but the data available in a short period of time is scarce, which leads to machine learning may not have a precise result. How to get more information from a few samples has become an important issue. Therefore, the main purpose of this research is to develop a generative adversarial network for learning with small numerical datasets. This research will use Wasserstein GAN (WGAN) as the GAN architecture of this research, and use mega trend-diffusion (MTD) to limit the bound of virtual samples that GAN generates, and use Back-propagation Network , BPN) as the generation network and the discrimination network of GAN, and called WGAN_MTD. This research chooses two datasets in UC Irvine Machine Learning Repository and evaluated and compared the performance by three criteria, such as Accuracy, standard deviation, and P-value. The experiment result shows that through this improved GAN architecture (WGAN_MTD), small sample data can also be used to generate virtual samples similar to real samples through GAN.

葉怡成，類神經網路模式應用與實作，儒林圖書公司，2003年三月。
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