應用粒子群演算法改善傳統二次曲面擬合區域性大地起伏－－以台中地區為例

Applying Particle Swarm Optimization to Improve Traditional Quadratic Surface Fitting Local Geoidal Undulation Model-A Case Study in Taichung Area

本研究結合實驗區現有已施測一等水準點正高及水準點上GPS測得之平面坐標和橢球高，以粒子群演算法改善傳統二次曲面擬合區域性大地起伏精度，研究結果顯示，採用粒子群演算法改善傳統二次曲面所建立之區域性大地起伏模型，其檢核點的均方根誤差精度可達到±1.02 cm，且不須計算任何參數。 This study intends to integrate the existing 1st order leveling data, GPS coordinates and ellipsoidal height to find out the geoidal undulation model of Taichung city. The main simulation is done by adopting Particle Swarm Optimization to better fit local geoid by traditional quadratic surface. In accordance with the experiment, we propose an improved result by using the Particle Swarm Optimization and showing a computed Root Mean Square Error about ±1.02cm without imposing any parametrical restrictions.