類神經網路由於具有學習訓練能力，常被使用於複雜的非線性系統識別與預測上，其中倒傳遞網路被廣泛的應用在各個領域，但是卻有收斂速度緩慢以及容易收斂至局部極小值的問題。
本文針對倒傳遞網路學習率及性能函數的改變對於網路的影響作初步的探討，以標準倒傳遞網路中之比例共軛梯度演算法和Levenberg- Marquardt演算法作收斂情況之比較；最後以迴歸分析法驗證其穩定性。

Neural Networks are often used in identification and prediction of complex nonlinear system due to its learning and training ability. Among them back-propagation neural network is widely applied in many fields. However, it suffers from a slow convergence rate and is susceptible to fall into local minima.
In this thesis, the effect of change of learning rate as well as the error function in back propagation network are discussed. Among them proportion conjugate gradient to develop the algorithm and Levenberg - Marquardt develops the algorithm to close collects comparison of the situation. The stability is also proved by regression analysis.

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