Universal approximation property of stochastic configuration networks for time series

Universal approximation property of stochastic configuration networks for time series

Abstract For the purpose of processing sequential data, such as time series, and addressing the challenge of manually tuning the architecture of traditional recurrent neural networks (RNNs), this paper introduces a novel approach-the Recurrent Stochastic Configuration Network (RSCN). This network is...

Deskribapen osoa

Xehetasun bibliografikoak
Egile Nagusiak:	Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
Formatua:	Artikulua
Hizkuntza:	English
Argitaratua:	Springer 2024-03-01
Saila:	Industrial Artificial Intelligence
Gaiak:	Recurrent neural networks Stochastic configuration networks Incremental learning Time series Deep learning
Sarrera elektronikoa:	https://doi.org/10.1007/s44244-024-00017-7

Antzeko izenburuak

Efficiency optimization methods for stochastic configuration networks
nork: Aijun Yan, et al.
Argitaratua: (2024-06-01)

Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams
nork: Pratama, Mahardhika, et al.
Argitaratua: (2021)

Multi-Task Learning Based on Stochastic Configuration Networks
nork: Xue-Mei Dong, et al.
Argitaratua: (2022-08-01)

Corrigendum: Multi-task learning based on Stochastic Configuration Networks
nork: Xue-Mei Dong, et al.
Argitaratua: (2023-09-01)

Vibration Signal Classification Using Stochastic Configuration Networks Ensemble
nork: Qinxia Wang, et al.
Argitaratua: (2024-06-01)

Leveraging Deep Learning for Time-Series Extrinsic Regression in Predicting the Photometric Metallicity of Fundamental-Mode RR Lyrae Stars
nork: Lorenzo Monti, et al.
Argitaratua: (2024-08-01)

Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network
nork: Kun Zhang, et al.
Argitaratua: (2023-04-01)

Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process
nork: Wei Zhou, et al.
Argitaratua: (2023-01-01)

FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction
nork: Yunqi Gao, et al.
Argitaratua: (2020-07-01)

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention
nork: Lexin Zhang, et al.
Argitaratua: (2023-09-01)

A Lightweight and Efficient Method of Structural Damage Detection Using Stochastic Configuration Network
nork: Yuanming Lu, et al.
Argitaratua: (2023-11-01)

A Gradient-Based Particle-Bat Algorithm for Stochastic Configuration Network
nork: Jingjing Liu, et al.
Argitaratua: (2023-02-01)

An End-to-End Adaptive Input Selection With Dynamic Weights for Forecasting Multivariate Time Series
nork: Lkhagvadorj Munkhdalai, et al.
Argitaratua: (2019-01-01)

Explainable Time-Series Prediction Using a Residual Network and Gradient-Based Methods
nork: Hyojung Choi, et al.
Argitaratua: (2022-01-01)

Adaptive Threshold Hierarchical Incremental Learning Method
nork: Xingyu Li, et al.
Argitaratua: (2023-01-01)

Efficient Incremental Learning Using Dynamic Correction Vector
nork: Yun Xiang, et al.
Argitaratua: (2020-01-01)

PFformer: A Time-Series Forecasting Model for Short-Term Precipitation Forecasting
nork: Luwen Xu, et al.
Argitaratua: (2024-01-01)

Working condition recognition of fused magnesium furnace based on stochastic configuration networks and reinforcement learning
nork: Weitao Li, et al.
Argitaratua: (2024-03-01)

Comparative Study of Physics-Based Modeling and Neural Network Approach to Predict Cooling in Vehicle Integrated Thermal Management System
nork: Duwon Choi, et al.
Argitaratua: (2020-10-01)

A novel approach for scene classification from remote sensing images using deep learning methods
nork: Xiaowei Xu, et al.
Argitaratua: (2020-10-01)

Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis
nork: Xiangrui Meng, et al.
Argitaratua: (2022-03-01)

Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data
nork: Haobo Lyu, et al.
Argitaratua: (2018-03-01)

A Windowed Correlation-Based Feature Selection Method to Improve Time Series Prediction of Dengue Fever Cases
nork: Tanvir Ferdousi, et al.
Argitaratua: (2021-01-01)

Experimental evaluation of stochastic configuration networks: is SC algorithm inferior to hyper-parameter optimization method?
nork: Hu, Minghui, et al.
Argitaratua: (2022)

Periodic Time Series Data Analysis by Deep Learning Methodology
nork: Haolong Zhang, et al.
Argitaratua: (2020-01-01)

Deep Time Series Forecasting Models: A Comprehensive Survey
nork: Xinhe Liu, et al.
Argitaratua: (2024-05-01)

TSViz: Demystification of Deep Learning Models for Time-Series Analysis
nork: Shoaib Ahmed Siddiqui, et al.
Argitaratua: (2019-01-01)

Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study
nork: Shahrokh Shahi, et al.
Argitaratua: (2022-06-01)

Tuning of Elasticsearch Configuration: Parameter Optimization Through Simultaneous Perturbation Stochastic Approximation
nork: Hårek Haugerud, et al.
Argitaratua: (2022-05-01)

Study on Stochastic and Autoregressive Time Series Forecasting for Hydrogen Refueling Station
nork: Ji-Wook Kim, et al.
Argitaratua: (2023-01-01)

FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification
nork: Robert D. Chambers, et al.
Argitaratua: (2020-04-01)

Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning
nork: Chenxi Ding, et al.
Argitaratua: (2021-11-01)

ANOMALY DETECTION ON TIME SERIES WITH WASSERSTEIN GAN APPLIED TO PHM
nork: Mélanie Ducoffe, et al.
Argitaratua: (2019-12-01)

A Neural Networks Based Method for Multivariate Time-Series Forecasting
nork: Shaowei Li, et al.
Argitaratua: (2021-01-01)

Configurable Model for Sigmoid and Hyperbolic Tangent Functions
nork: Khaled Salah, et al.
Argitaratua: (2023-07-01)

An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation
nork: Sanyi Li, et al.
Argitaratua: (2025-01-01)

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy
nork: Liuhuan Ma, et al.
Argitaratua: (2024-05-01)

TimeREISE: Time Series Randomized Evolving Input Sample Explanation
nork: Dominique Mercier, et al.
Argitaratua: (2022-05-01)

CDS risk premia forecasting with multi-featured deep RNNs: An application on BR[I]CS countries
nork: Yasin Kutuk
Argitaratua: (2023-11-01)

Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan
nork: Muhammad Ishfaque, et al.
Argitaratua: (2022-04-01)