A deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization
出版年份 2022 全文链接
标题
A deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization
作者
关键词
-
出版物
COMPUTATIONAL INTELLIGENCE
Volume -, Issue -, Pages -
出版商
Wiley
发表日期
2022-10-28
DOI
10.1111/coin.12556
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- An adaptive Drop method for deep neural networks regularization: Estimation of DropConnect hyperparameter using generalization gap
- (2022) El Houssaine Hssayni et al. KNOWLEDGE-BASED SYSTEMS
- Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification
- (2021) Mehak Khan et al. JOURNAL OF SUPERCOMPUTING
- Deep recurrent neural networks with finite-time terminal sliding mode control for a chaotic fractional-order financial system with market confidence
- (2021) Yong-Long Wang et al. CHAOS SOLITONS & FRACTALS
- Deep neural network-based generalized sidelobe canceller for dual-channel far-field speech recognition
- (2021) Guanjun Li et al. NEURAL NETWORKS
- Simulation of photo-voltaic power generation using copula autoregressive models for solar irradiance and air temperature time series
- (2021) Andres Felipe Ramírez et al. RENEWABLE ENERGY
- Analyzing ecological environment change and associated driving factors in China based on NDVI time series data
- (2021) Luguang Jiang et al. ECOLOGICAL INDICATORS
- KRR-CNN: kernels redundancy reduction in convolutional neural networks
- (2021) El houssaine Hssayni et al. NEURAL COMPUTING & APPLICATIONS
- Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
- (2020) Omer Berat Sezer et al. APPLIED SOFT COMPUTING
- Knowledge extraction from deep convolutional neural networks applied to cyclo-stationary time-series classification
- (2020) Diego Cabrera et al. INFORMATION SCIENCES
- Time series classification based on multi-feature dictionary representation and ensemble learning
- (2020) Bing Bai et al. EXPERT SYSTEMS WITH APPLICATIONS
- Deep learning for time series classification: a review
- (2019) Hassan Ismail Fawaz et al. DATA MINING AND KNOWLEDGE DISCOVERY
- A deep learning framework for time series classification using Relative Position Matrix and Convolutional Neural Network
- (2019) Wei Chen et al. NEUROCOMPUTING
- Classification of chaotic time series with deep learning
- (2019) Nicolas Boullé et al. PHYSICA D-NONLINEAR PHENOMENA
- Mathematical mixed-integer programming for solving a new optimization model of selective image restoration: modelling and resolution by CHN and GA
- (2018) Nour-eddine Joudar et al. CIRCUITS SYSTEMS AND SIGNAL PROCESSING
- A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network
- (2017) Arash Gharehbaghi et al. IEEE Transactions on Neural Networks and Learning Systems
- Piecewise two-dimensional normal cloud representation for time-series data mining
- (2016) Weihui Deng et al. INFORMATION SCIENCES
- Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles
- (2015) Anthony Bagnall et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- The BOSS is concerned with time series classification in the presence of noise
- (2014) Patrick Schäfer DATA MINING AND KNOWLEDGE DISCOVERY
- A Bag-of-Features Framework to Classify Time Series
- (2013) M. G. Baydogan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A time series forest for classification and feature extraction
- (2013) Houtao Deng et al. INFORMATION SCIENCES
- A new cognitive model: Cloud model
- (2009) Deyi Li et al. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started