GPS 單點定位或稱絕對定位，傳統上使用虛擬距離觀測量，容易受到 電離層延遲影響，導致定位精度較差。因此，本文的目的為構建即時的區 域性電離層模型，以便能夠即時減弱電離層延遲量，提高單頻GPS 單點定 位的精度。 構建電離層模型的方法有很多種，而運用類神經網路為可能方法之一， 但是， 國內較少人探討。本研究嘗詴使用倒傳遞類神經網路(Back-propagation Artificial Neural Network)，構建即時的區域電離層模型，藉由選擇適當的神經訓練函數及隱藏層神經元，利用過去收集的已知參考站的雙頻GPS 資料，計算電離層延遲量，訓練類神經網路，直到精度合乎要求；再以檢核站GPS 資料，檢驗類神經網路預測電離層延遲的功效。 採用的實驗資料為臺南市政府e-GPS 系統所提供六個測站，2008 年1 月3 日到1 月5 日的GPS 資料，計算測站與GPS 衛星連線中假想的電離層 薄殼交點—電離層穿透點(Ionosphere Pierce Point, IPP）之地理位置(緯度φ、經度λ)，及太陽黑子數(sunspot numbers)等當作輸入值，IPP 的垂直電離層延遲當作輸出值，測詴包含單日、兩日以及不同的資料型態(IPP 點、網格點)等情況訓練類神經網路，藉由相對應的驗證資料，檢驗類神經網路的功效，最後將類神經網路的預估成果與全球電離層改正模型、雙頻GPS 資料計算的電離層延遲相比較，並根據改正率與統計特性，評估類神經網 路構建出的區域性電離層模型的成效。 由實驗成果顯示，構建的即時區域性電離層模型的標準差可小於±3TECU，並可改正約80%的電離層延遲誤差，故以類神經網路可有效的構 建出區域性的電離層模型。 / The conventional single point positioning using GPS pseudo rangemeasurements, are vulnerable to ionospheric errors, leading to poor positioningaccuracy. Constructing a real-time ionospheric model is one of the methods that can reduce the ionospheric errors and improve the single point positioning accuracy. Although there are many methods to construct regional ionosphere model,using artificial neural network (ANN) to construct a real-time ionospheric model is less to be mentioned. This study used back-propagation artificial neural network to estimate a regional real-time ionospheric model by selecting the appropriate training functions and the number of hidden layers and its’ nodes. The neural network had to be ‘trained’ by the computed TECs from reference stations’ duel-frequency GPS data until the required accuracy was achieved. The experimental data are collected from 6 e-GPS stations of Tainan city government on January 3 to January 5, 2008. The input values for the ANN includ the geographical location of the ionosphere pierce point (IPP) and solar activity (sunspot number). The output value are those IPPs’ vertical total electron content (VTEC). Different times range and data types (IPPs’ or raster data) for the impact of the ANN are tested. And then compared to Klobuchar model and global ionopheric model, according to the correct rate and the ΔTEC statistic table decide the effectiveness of ANN. According to the test results, the regional ionopheric model constructed by ANN can corrected 80% of the ionospheric errors, the standard deviation of ΔTEC is less than ±3TECU.