本研究提出結合人工智慧演算法(artificial Intelligence algorithm, AI)與加權幾何精度因子的方式，以改善無線定位系統之精確度。透過人工智慧演算法近似加權幾何精度因子，以進行最佳量測單位集合之挑選，透過選擇最佳量測單位之集合進行估測行動台位置，可以獲得較高之定位經確度與效益，並探討人工智慧演算法於無線定位中之效益與輸出輸入映射關係之成果。後續進而接收全球定位系統（Global Positioning System, GPS）之衛星定位資訊，與類神經網路(neural network, NN )之中進行資料模型之訓練，以驗證研究方式之估測正確性。
類神經網路運用仿造生物神經連接運算能力的方式來找尋最佳化解答，將此特性用於搜索量測單位求得之行動台估測範圍之中，探討類神經網路於無線定位系統中提高定位精確度之研究。類神經網路具有學習能力、容錯能力以及平行運算能力，藉其特性於量測單位之行動台估測範圍內找尋最佳估測位置，並探討類神經網路之輸入/輸出映射關係。本計畫之架構可應用於全球定位系統、無線感測網路（wireless sensor networks, WSN）以及行動通訊系統(cellular communication systems)之中。

This study proposes a combination of artificial intelligence algorithm (AI) and weighted geometric precision factor to improve the accuracy of the wireless positioning system. The weighted geometric precision factor is approximated by the artificial intelligence algorithm to select the best measurement unit set. By selecting the optimal measurement unit set to estimate the action table position, higher positioning accuracy and benefit can be obtained, and the discussion can be made. The result of the relationship between the benefit of the artificial intelligence algorithm and the output input in wireless positioning. Subsequently, the satellite positioning information of the Global Positioning System (GPS) is received, and the data model is trained in the neural network (NN) to verify the correctness of the research method.
The neural network uses the method of counterfeiting the biological neural connection computing ability to find the optimal solution. This feature is used in the estimation range of the mobile station obtained by the search measurement unit to explore the neural network in the wireless positioning system. Improve research on positioning accuracy. Neural network has learning ability, fault tolerance and parallel computing capabilities, and we use it for mobile station position estimation. Because of the characteristics of the neural network, we proposed using neural network to find the best estimation position of the motion station, and will explore the mapping relationships between input and the output in our study. The architecture we proposed in this research project can be applied to the global positioning system, wireless sensing network and mobile communication system.

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