Indoor position location based on cascade correlation networks

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 cascade correlation network for an indoor position location system, and provide location service for user. In the first part, we will collect the RSS information of reference point to train the hybrid neural network models, and input the RSS information of track object to the model, and the model will provide the location of track object according to the RSS information. In the second part, we will calculate the performance of each neural network models and their weights were modified according to performance of each neural network. We will test the accuracy of location system again, and will use this system for patient care, smart home, and smart space.