由於倒傳遞類神經網路的一般化的映射能力，目前已經在工業中得到廣泛應用，然而，它可能涉及許多超參數要做調整，包含權重初始值、活化函數、學習率等等，在選擇這些參數時通常遵循試誤法，但這要花費很多時間，且模型最後的輸出結果也可能不正確。為了解決這個問題，本研究提出了一套調整超參數的程序來幫助建模時可以選擇適當的超參數以達到較高的映射準確率。
本研究參考許多類神經網路超參數設定值的相關文獻，包含隱藏層層數與神經元數、活化函數、權重初始值與優化方法、批量大小、正規化方法及batch normalization的技巧，以建構我們所提出的深度神經網路模型來處理迴歸問題，在此，迴歸問題是指找到一個可以表示給定數據集的輸入及輸出關係的模型。深度神經網絡可以解決淺層結構之倒傳遞類神經網路的問題，例如收斂到局部最小值而非全域最小值、收斂速度太慢與訓練時間太長等。本研究使用18種不同的資料集來比較深度神經網路與具有淺層架構之倒傳遞類神經網路的表現，這些資料集具有各種不同組合的數據大小、複雜度以及輸入特徵的數目。研究結果顯示本研究所提出的深度神經網路在預測準確率方面的表現比淺層架構的倒傳遞類神經網路為佳，此外，由於有early stopping的機制，此模型進行訓練時所需的epoch數也比較少，而early stopping亦可避免神經網路發生過度擬合的問題。至於訓練模型時所需的時間，此模型在大多數資料集中都省下大量的時間，這也加速了本研究的建模過程。最後，本研究亦透過個案分析來展現此深度神經網路模型的表現。

Due to its general pattern-mapping capability, back propagation neural network (BPNN) has been widely used in industry. However, BPNN can involve many hyper-parameters, including weights initialization, activation function, and learning rate. A trial-and-error approach is usually followed in selecting these parameters, which makes it time consuming and sometimes may provide inaccurate results. To overcome this challenge, this study proposes a tuning procedure to aid in selecting appropriate hyper-parameters when modeling to achieve higher mapping accuracy.
This study refers to lots of related work about artificial neural network (ANN) hyper-parameters including the number of hidden layers and units, activation function, weights initialization and optimization method, mini-batch size, regularization, and batch normalization technique to build our proposed model deep NN for regression problems. Here, the regression problem is to find a model that can represent the input-output relationship for a given dataset. Deep NN can resolve the problems of BPNN with shallow structure such as converge to local minima rather than global minima, slow convergence rate, and long training time. Eighteen different datasets with various combination of data size, complexity, and number of features are then used to compare deep NN with shallow BPNN. Through the performance analysis, our proposed deep NN is superior to BPNN with shallow structure in terms of the accuracy. Besides, it needs a smaller number of epochs due to early stopping technique which can also prevent networks from overfitting. As for the training time, deep NN greatly saves the large amount of time for most of the datasets and this really speeds up the modeling process. In the end, a case study is also demonstrated to show the performance of our model.

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