TY - JOUR
T1 - An evolving-construction scheme for fuzzy systems
AU - Wang, Di
AU - Zeng, Xiao Jun
AU - Keane, John A.
N1 - Funding Information:
Manuscript received December 30, 2009; accepted March 3, 2010. Date of publication April 12, 2010; date of current version August 6, 2010. This work was supported by the U.K. Engineering and Physical Sciences Research Council under Grant EP/C513355/1. D. Wang is with the Department of Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, U.K. (e-mail: [email protected]). X.-J. Zeng is with the School of Computer Science, University of Manchester, Manchester, M13 9PL, U.K. (e-mail: [email protected]). J. A. Keane is with the School of Computer Science, University of Manchester, Manchester, M13 9PL, U.K. (e-mail: john.keane@manchester. ac.uk). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TFUZZ.2010.2047949
PY - 2010/8
Y1 - 2010/8
N2 - This paper proposes an evolving-construction scheme for fuzzy systems (ECSFS). ECSFS begins with a simple fuzzy system and evolves its structure by adding more fuzzy terms and rules to achieve a better accuracy in a "greedy" way. An interesting feature of ECSFS is that it is able to automatically locate mathematically meaningful points, such as the extremum and inflexion points of the approximated function one by one, and then adds fuzzy terms based on these points. Fuzzy systems with such extreme points, like their fuzzy terms, are more efficient than other fuzzy systems by using the same number of fuzzy rules. As a result, ECSFS often achieves a better accuracy for fuzzy-system identification compared with the previous methods when using the same number of fuzzy rules. A number of simulation results are given to illustrate the advantages of the proposed scheme.
AB - This paper proposes an evolving-construction scheme for fuzzy systems (ECSFS). ECSFS begins with a simple fuzzy system and evolves its structure by adding more fuzzy terms and rules to achieve a better accuracy in a "greedy" way. An interesting feature of ECSFS is that it is able to automatically locate mathematically meaningful points, such as the extremum and inflexion points of the approximated function one by one, and then adds fuzzy terms based on these points. Fuzzy systems with such extreme points, like their fuzzy terms, are more efficient than other fuzzy systems by using the same number of fuzzy rules. As a result, ECSFS often achieves a better accuracy for fuzzy-system identification compared with the previous methods when using the same number of fuzzy rules. A number of simulation results are given to illustrate the advantages of the proposed scheme.
KW - Evolving construction
KW - function approximation
KW - greedy algorithm
KW - incremental learning
UR - http://www.scopus.com/inward/record.url?scp=77955504076&partnerID=8YFLogxK
U2 - 10.1109/TFUZZ.2010.2047949
DO - 10.1109/TFUZZ.2010.2047949
M3 - Article
AN - SCOPUS:77955504076
SN - 1063-6706
VL - 18
SP - 755
EP - 770
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 4
M1 - 5446334
ER -