TY - GEN
T1 - Complex fuzzy computing to time series prediction - A multi-swarm PSO learning approach
AU - Li, Chunshien
AU - Chiang, Tai Wei
PY - 2011
Y1 - 2011
N2 - A new complex fuzzy computing paradigm using complex fuzzy sets (CFSs) to the problem of time series forecasting is proposed in this study. Distinctive from traditional type-1 fuzzy set, the membership for elements belong to a CFS is characterized in the unit disc of the complex plane. Based on the property of complex-valued membership, CFSs can be used to design a neural fuzzy system so that the system can have excellent adaptive ability. The proposed system is called the complex neuro-fuzzy system (CNFS). To update the free parameters of the CNFS, we devise a novel hybrid HMSPSO-RLSE learning method. The HMSPSO is a multi-swarm-based optimization method, first devised by us, and it is used to adjust the premise parameters of the CNFS. The RLSE is used to update the consequent parameters. Two examples for time series foresting are used to test the proposed approach. Through the experimental results, excellent performance has been exposed.
AB - A new complex fuzzy computing paradigm using complex fuzzy sets (CFSs) to the problem of time series forecasting is proposed in this study. Distinctive from traditional type-1 fuzzy set, the membership for elements belong to a CFS is characterized in the unit disc of the complex plane. Based on the property of complex-valued membership, CFSs can be used to design a neural fuzzy system so that the system can have excellent adaptive ability. The proposed system is called the complex neuro-fuzzy system (CNFS). To update the free parameters of the CNFS, we devise a novel hybrid HMSPSO-RLSE learning method. The HMSPSO is a multi-swarm-based optimization method, first devised by us, and it is used to adjust the premise parameters of the CNFS. The RLSE is used to update the consequent parameters. Two examples for time series foresting are used to test the proposed approach. Through the experimental results, excellent performance has been exposed.
KW - Complex fuzzy set (CFS)
KW - Complex neuro-fuzzy system (CNFS)
KW - Hierarchical multi-swarm particle swarm optimization (HMSPSO)
KW - Recursive least square estimator (RLSE)
KW - Time series forecasting
UR - http://www.scopus.com/inward/record.url?scp=84867874969&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-20042-7_25
DO - 10.1007/978-3-642-20042-7_25
M3 - 會議論文篇章
AN - SCOPUS:84867874969
SN - 9783642200410
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 242
EP - 251
BT - Intelligent Information and Database Systems - Third International Conference, ACIIDS 2011, Proceedings
T2 - 3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011
Y2 - 20 April 2011 through 22 April 2011
ER -