Hierarchical Evolving Fuzzy System: A Method for Multidimensional Chaotic Time Series Online Prediction

被引：9

作者：

Hu, Lei ^{[1
]}

Xu, Xinghan ^{[2
]}

Ren, Weijie ^{[3
]}

Han, Min ^{[4
]}

机构：

[1] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China

[2] Dalian Univ Technol, Fac Infrastruct Engn, Dalian 116024, Peoples R China

[3] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China

[4] Dalian Univ Technol, Profess Technol Innovat Ctr Distributed Control In, Key Lab Intelligent Control & Optimizat Ind Equipm, Minist Educ, Dalian 116024, Peoples R China

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2024年 / 32卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Kernel; Time series analysis; Fuzzy systems; Predictive models; Data models; Adaptation models; Convergence; Evolving fuzzy system (EFS); hierarchical; kernel conjugate gradient (KCG); multidimensional chaotic time series; online prediction; sparse learning strategy; CLASSIFICATION; IDENTIFICATION;

D O I：

10.1109/TFUZZ.2023.3348847

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Evolving fuzzy system (EFS), a special adaptive model with Takagi-Sugeno fuzzy rules that can adaptively update internal parameters based on data streams, has been widely used in online learning scenarios. However, current EFSs are mainly single-layer models, which cannot adequately capture hidden information in multidimensional chaotic time series. To perform online prediction of multidimensional chaotic time series, a novel evolving fuzzy system, called hierarchical evolving fuzzy system with kernel conjugate gradient (HEFS-KCG), is proposed in this article. HEFS-KCG excavates and captures latent evolutionary patterns concealed within dynamic systems through a layer-by-layer processing of multidimensional information. HEFS-KCG performs structural evolution based on data distribution in the antecedent part, and combines the sparse learning strategy and kernel conjugate gradient to update the consequent parameters. Subsequently, we provide a theoretical analysis of HEFS-KCG, ensuring its convergence when applied to online prediction. The simulation results demonstrate that HEFS-KCG outperforms existing EFSs and other models for multidimensional chaotic time series online prediction.

引用

页码：3329 / 3341

页数：13

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