Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology


Self-Adaptive Pattern Recognition Based on Multi-Agent

1Jianhua Liu and 2Xianyi Cheng
1School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
2College of Computer Science and Technology, Nantong University, Nantong Jiangsu 226019, China
Research Journal of Applied Sciences, Engineering and Technology  2013  23:5420-5424
http://dx.doi.org/10.19026/rjaset.5.4211  |  © The Author(s) 2013
Received: November 24, 2012  |  Accepted: January 17, 2013  |  Published: May 28, 2013

Abstract

Pattern recognition consists in finding a correspondence between patterns and their prototypes. Intrinsically, it is a distributed process in terms of goals to be reached, zones to be processed and methods to be applied. In this paper, a multi-agent based self-adaptive pattern recognition framework is proposed to cope with the difficulties in the procedure. Each agent is dedicated to recognize a single kind of pattern and communicate with other agents. The cooperation between them reinforces the object-model correspondence hypothesis of each agent and leads to the self-adaptation of their recognition results in order to reach a consistent and integrated interpretation of the whole image. The experiment result validates this approach especially in the flexibility and expansibility of the system.

Keywords:

Cooperation, multi-agent systems, pattern recognition, self-adaptation,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved