文献类型：期刊文献

中文题名：基于误差分布补偿的混杂模糊神经网络时间序列预测模型

英文题名：Hybrid Fuzzy Neural Network Based on Error Distribution Analysis for Time-Series Prediction Model

作者：安杰[1];王梦灵[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2023

卷号：49

期号：4

起止页码：589

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

基金：上海市“科技创新计划”人工智能专项(19DZ1209003)。

语种：中文

中文关键词：模糊神经网络;混合网络结构;误差分布分析;损失函数;时间序列

外文关键词：fuzzy neural networks;hybrid network structure;error distribution analysis;loss function.;time-series

摘要：针对时间序列预测问题,提出了一种基于误差分布分析的混杂模糊神经网络预测模型。首先,提出了一种混杂模糊神经网络结构,将原单一输出层替换为由一个全连接层和非线性激活函数混合的组合网络,用于学习组合隶属度层的输出。然后,提出了基于误差分布的损失函数,使得更新参数的过程中既考虑了误差的大小又考虑了期望的误差分布。根据新的模型结构和新的损失函数,在梯度下降过程中,预测误差小出现概率较高或误差大出现率低的两类样本将获得较小的梯度更新增益,其他样本将在训练过程中获得较大更新增益,本文提出的方法可以获得更均匀和稳定的预测输出。最后通过两个仿真实验验证了本文提出的方法的有效性和准确性。
This paper considers a hybrid fuzzy neural networks(HFNN)for time-series prediction based on error distribution analysis.Firstly,a new hybrid FNN(HFNN)structure is established,where the last two layers are replaced by a combination of a full connection layer and nonlinear activation function.Thus,more parameters can be updated in training process to guarantee the prediction accuracy.Secondly,a novel attention loss function is proposed to make a sample with a certain error distribution,leading to gains in training process.Rule analysis with probability density function,indicates that the proposed method can provide a more uniform and stable prediction output.The prediction errors of HFNN converge to a compact set.Finally,two benchmark problems are applied to demonstrate the hybrid model performance on a time series prediction.The comparisons with other prediction models verify the efficiency and accuracy of the proposed HFNN model.