基于核共轭梯度演化模糊系统的混沌时间序列在线预测  

Online prediction of chaotic time series based on kernel conjugate gradient evolving fuzzy system

作　　者：胡磊 韩敏[2] HU Lei;HAN Min(Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian 116024,China;Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学电子信息与电气工程学部,辽宁大连116024 [2]大连理工大学教育部工业设备智能控制与优化重点实验室,辽宁大连116024

出　　处：《控制与决策》2024年第9期3099-3107,共9页Control and Decision

基　　金：国家自然科学基金项目(62173063)。

摘　　要：为了增强演化模糊系统在混沌时间序列中的在线预测性能,提出一种基于核共轭梯度的演化模糊系统模型.该模型通过基于结构演化的前件部分,挖掘并捕获隐藏在时间序列中的模糊规则.同时,基于参数更新的后件部分有机结合稀疏学习策略和核共轭梯度算法,以降低计算复杂度和提高模型收敛性能.基于核共轭梯度的演化模糊系统不仅剔除了样本中的冗余信息,而且有效平衡了模型的预测精度和预测效率.实验结果表明,所提模型在标杆和真实混沌时间序列的在线预测任务中具有有效性.To enhance the online prediction performance of evolutionary fuzzy systems in chaotic time series,an evolving fuzzy system based on kernel conjugate gradient(EFS-KCG)is proposed.The model explores and captures the fuzzy rules hidden in the time series through a structural evolution-based antecedent part.Meanwhile,the consequent part based on parameter update organically combines sparse learning strategy and kernel conjugate gradient algorithm to reduce the computational complexity and improve the convergence performance of the model.The EFS-KCG not only eliminates the redundant information in the samples,but also effectively balances the prediction accuracy and efficiency of the model.Experiment results show that the proposed model is effective in online prediction tasks for both benchmark and real chaotic time series.

关 键 词：混沌时间序列 在线预测 演化模糊系统 模糊规则 稀疏学习策略 核共轭梯度 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]