小功率太陽能系統常被應用於獨立的系統中，如LED照明設備。這些系統需具有構造簡單且符合高經濟效益的太陽能最大功率追蹤之技術，其中以雙直線方程式近似法和曲線方程式近似法之類比式最大功率追蹤技術為首選，其優點為構造簡單、成本低、具有快速的追蹤及高效率的追蹤效果；不過，這些太陽能最大功率點追蹤之技術通常是由複雜的數值模擬和實驗結果來獲得太陽能電池的輸出電壓和輸出電流之關係式。
為了協助系統設計者並簡化設計之流程，在這篇論文中提出以類神經網路之方式，來訓練出最大功率追蹤技術中之雙直線方程式近似法和曲線方程式近似法的參數，此類神經網路模型之輸入參數僅需為太陽能電池中最大功率點之電壓(VMP)、最大功率點之電流(IMP)、開路電壓(VOC)、短路電流(ISC)及太陽能電池模組的電池數(NS)，這些參數很容易從太陽能電池規格書中來獲得。論文中收集了現今( 2010年)前十大太陽能模組製造廠商所生產之太陽能模組規格書後，藉由類神經網路模組的模擬來獲得雙直線方程式和曲線方程式的參數，由模擬結果顯示，雙直線方程式近似法和曲線方程式近似法所建立之類神經網路模型可以準確的預測雙直線方程式及曲線方程式之參數，且兩者的參數最大誤差皆小於10%，證明類神經網路模擬應用於追蹤太陽能電池最大功率點上有良好的效能。

Low power PV systems are commonly used in stand-alone applications such as LED lightings. For these systems, a simple and cost-effective maximum power point tracking (MPPT) solution is essential.For low power PV systems, analog MPPT techniques such as two-line approximation (TLA) method and curve approximation (CA) method are preferred due to their simple structure, low cost, fast tracking speed and high tracking efficiency.However, these methods exploit the relationship between the values of panel voltage and current, which often should be obtained by complicated numerical simulations or experiments.
To assist the system designer and simplify the design procedure, an artificial neural network (ANN) based method is proposed in this thesis to obtain the required parameters of the TLA MPPT method and CA MPPT method directly. The input variables are the nominal open-circuit voltage (VOC,STC), the nominal short-circuit current (ISC,STC), the voltage at the MPP (VMP), the current at the MPP (IMP) and the cell number (NS) in the utilized PV array. These data can easily be obtained from datasheets of PV panels. After collecting all the PV panel datasheets of the top 10 PV panel manufacturers in 2010, simulations are made to obtain the parameters of the TLA and CA methods. These data are then utilized as the training data of the proposed ANN. Simulation results show that the proposed ANN can precisely predict the parameters of the TLA and CA methods, with largest error smaller than 10%.

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