Evolving Fuzzy Classification System by a Quantum Particle Swarm Optimization Algorithm

DOI

10.2991/icmemtc-16.2016.30How to use a DOI?

Keywords

fuzzy classification system; fuzzy rules; quantum evolving algorithm; quantum particle swarm optimization.

Abstract

This paper discusses how to construct a fuzzy classification system (FCS) effectively and accurately. In the FCS, the initial fuzzy rules are optimized with a quantum bit which has many unique advantages such as small population size, fast convergence, short training time and strong global search ability. After then, in order to accomplish the optimization for the fuzzy space partition and the number of fuzzy rules, this paper propose a method-quantum particle swarm optimization (QPSO) -to improve the initial FCS. The experiment result demonstrates that this method is more efficient than other methods without QPSO.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yunhui Zhu
AU  - Jun Sun
PY  - 2016/04
DA  - 2016/04
TI  - Evolving Fuzzy Classification System by a Quantum Particle Swarm Optimization Algorithm
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 160
EP  - 168
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.30
DO  - 10.2991/icmemtc-16.2016.30
ID  - Zhu2016/04
ER  -