| Summary: | 碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 101 === In the recent year, the position location system with ubiquitous computing has become very important, and the use of technology in the position location system has increasingly become the object of study and enterprise applications. One of the rapidly advancing technologies of position location system research is the global positioning system (GPS) but in indoor environments, the receiver may not receive the signal because the signal is subject to the building’s impact. This congenital limitation renders the GPS unusable for the indoor position location system. In this paper, we will use multiple Back-Propagation neural networks for a radio frequency identification (RFID) indoor location system, and provide location service for user. In the first part, we will collect the RSS information of reference point to train the neural network models. In the second part, genetic algorithm (GA) is used to find the weight of each neural network according to performance of each neural network. The weigth is used for integrating output results of each neural network. Finally, we input the RSS information of track object to the model that will provide the location of track object according to the RSS information. The location will be integrating by the weight of GA. We conducted this experiment to prove that our methodology can provide better accuracy than the single neural network. We will use this system III for patient care, smart home, and smart space.
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