Complex Fuzzy Computing to Time Series Prediction - A Multi-Swarm PSO Learning Approach

被引：0

作者：

Li, Chunshien ^{[1
]}

Chiang, Tai-Wei ^{[1
]}

机构：

[1] Natl Cent Univ, Lab Intelligent Syst & Applicat, Dept Informat Management, Chungli, Taiwan

来源：

INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2011, PT II | 2011年 / 6592卷

关键词：

complex fuzzy set (CFS); complex neuro-fuzzy system (CNFS); hierarchical multi-swarm particle swarm optimization (HMSPSO); recursive least square estimator (RLSE); time series forecasting; NETWORKS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：242 / 251

页数：10

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