遞歸神經網路應用於接收訊號強度指標之路徑預測模型;Recursive Neural Network Applied to Receive Signal Strength for Path Prediction Model

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Title:	遞歸神經網路應用於接收訊號強度指標之路徑預測模型;Recursive Neural Network Applied to Receive Signal Strength for Path Prediction Model
Authors:	吳思慧;WU, SHIH-HUI
Contributors:	資訊工程學系碩士在職專班
Keywords:	遞歸神經網路;接收訊號強度指標;機器學習;Recursive Neural Network;RSSI;Machine learning
Date:	2017
Issue Date:	2019-07-17
Publisher:	資訊工程學系碩士在職專班
Abstract:	基於低功耗藍牙無線訊號發射器 iBeacon 的微定位技術，相關文獻多在探討解決室內定位 RSS 訊號飄移，產生定位不良的方法。本論文研究應用遞歸神經網路架構的路徑預測模型，將基於接收訊號強度指標定位的路徑資料，結合最近鄰居法及權重演算法，計算歐幾里德距離得出定位點，並可識別訊號飄移的定位點，實現室內定位的目標。輸入具時間序列的 RSSI 訊號強度資料，可得出整個路徑軌跡。 In recently, with the development of the cloud technology and the cloud services requirement, and popularity of mobile devices, EMC Digital Universe study expects data explosion to see ten-fold increase by 2020. The cloud data is lead to explore the application of artificial intelligence and machine learning technology Pass studied, Apply in RSSI of the indoor position system used the BLE devices, iBeacon, most previous relate work were proposed the algorithm or method in order to improve the accuracy of the position.In our research, we proposed training the recursive neural network applied the RSSI signal information and integrate K-NN algorithm and Euclidean Distance, the model can prediction next position with the RSSI signal and the broadcast position
Appears in Collections:	[資訊工程學系] 學位論文

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