Neural based RBF and LVQ Network Model of Knowledge Representation in the Prediction of Mobile Location

  • Authors

    • J. Venkata Subramanian
    • S. Govindarajan
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.22042
  • Neural network, LVQ, RBF, Mobile Location, Reality mining data.

  • Abstract

    In mobile communication system, mostly the Location based services and quality of services have need of information about the mobile station location. If the cellular communication system knows the movement of the subscriber is preplanned, and exceedingly passionate about the mobile subscriber’s personal characteristics. Thus prediction of mobile location is mainly essential matter to give the location based quality of service to the subscribers [8]. Neural network has several network models that can be utilized to predict mobile location and preparation parameters can be collect from the individual portability of the subscriber. In this paper our contribution is that RBF network techniques and LVQ be use to forecast the subscriber’s next locality based on the present locality [6]. The MATLAB software was making use of substantiate the constraints of Radial Basis Function network structure and also the similar training facts to LVQ network. At first, the execution of the LVQ (Learning Vector Quantization), RBF (Radial Basis function) [13] has been considered. Our real commitment in this paper is that we prepared neural system utilizing the data about adjusting cell and neighboring cells, collected from a drive analyzer Reality mining on specific ways demonstrating the genuine Mobile Station (MS) area.

     

     

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  • How to Cite

    Venkata Subramanian, J., & Govindarajan, S. (2018). Neural based RBF and LVQ Network Model of Knowledge Representation in the Prediction of Mobile Location. International Journal of Engineering & Technology, 7(4.19), 172-176. https://doi.org/10.14419/ijet.v7i4.19.22042

    Received date: 2018-11-28

    Accepted date: 2018-11-28

    Published date: 2018-11-27