Title :
Based on RPCL Object Position Clustering under Cellular Network
Author :
Chen Hua-Sha ; Shen Jia-Jie
Author_Institution :
Inf. Technol. Center, Shanghai Int. Studies Univ., Shanghai, China
Abstract :
Aiming to problem how to cluster object position under cellular network situation, using the method of random selection and position, an improved RPCL (Rival Penalized Competitive Learning) algorithm is designed to handle this question, an improved RPCL algorithm is designed to handle this problem. Though theoretical derivation, the correctness of improved RPCL algorithm is proofed. The correctness of improved RPCL algorithm and theoretical derivation is also verified by experiment.
Keywords :
learning (artificial intelligence); pattern clustering; town and country planning; RPCL object position clustering; cellular network situation; city object position clustering; random position method; random selection method; rival penalized competitive learning; smart city; Algorithm design and analysis; Cities and towns; Clustering algorithms; Educational institutions; Neural networks; Training; Vectors; RPCL algorithm; cellular network; position clustering; smart city;
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2014 Sixth International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-1-4799-3434-8
DOI :
10.1109/ICMTMA.2014.43
