本論文提出了前饋式多層感知機(multiple layer perceptron, MLP)與遞迴式神經網路(recurrent neural networks, RNN)之學習效果進行實現與比較，本論文所提出的遞迴神經網路係以狀態空間，並以區域回授的方式呈現，在倒傳遞演算法的計算下，我們發現區域回授的RNN在學習的效果上明顯優於傳統的MLP，最後我們以數值案例驗證了本論文所提方法的有效性。
In this thesis, a multiple layer perceptron (MLP) and a recurrent neural network (RNN) are proposed for investigation and comparison in the sense of learning performances. The proposed RNN is a local feedback network and is composed by a state space realization. By using back propagation learning algorithm, the learning performances of the proposed RNN is better than that of the conven-tional MLP. Finally, numerical examples are performed to illustrate the effec-tiveness of the proposed approach.
