For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Neural Network Location Based on Weight Optimization with Genetic Algorithm under the Condition of Less Information
Jian Hui WANG Jia Liang WANG Da Ming WANG Wei Jia CUI Xiu Kun REN
IEICE TRANSACTIONS on Communications
Publication Date: 2016/11/01
Online ISSN: 1745-1345
Type of Manuscript: PAPER
Category: Fundamental Theories for Communications
location, NLOS, neural network, genetic algorithm,
Full Text: PDF(1.2MB)>>
This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.