Deep learning with PID residual elimination network for time-series prediction of water quality in aquaculture industry

[1]  Maya L. Pai,et al.  A multivariate EMD-LSTM model aided with Time Dependent Intrinsic Cross-Correlation for monthly rainfall prediction , 2022, Appl. Soft Comput..

[2]  B. G. K. Mohan,et al.  A convolutional neural network based approach to financial time series prediction , 2022, Neural Computing and Applications.

[3]  Bob Zhang,et al.  Meta PID Attention Network for Flexible and Efficient Real-World Noisy Image Denoising , 2022, IEEE Transactions on Image Processing.

[4]  O. Kaynak,et al.  Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction , 2022, Eng. Appl. Artif. Intell..

[5]  Kemal Özkan,et al.  A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images , 2022, Neural Comput. Appl..

[6]  Daoliang Li,et al.  Modelling and controlling dissolved oxygen in recirculating aquaculture systems based on mechanism analysis and an adaptive PID controller , 2022, Comput. Electron. Agric..

[7]  Anil Kumar Tripathi,et al.  DNNAttention: A deep neural network and attention based architecture for cross project defect number prediction , 2021, Knowl. Based Syst..

[8]  Guido Sciavicco,et al.  A time series forecasting based multi-criteria methodology for air quality prediction , 2021, Appl. Soft Comput..

[9]  Xinhui Zhou,et al.  Simulation of future dissolved oxygen distribution in pond culture based on sliding window-temporal convolutional network and trend surface analysis , 2021, Aquacultural Engineering.

[10]  Yong Deng,et al.  Natural visibility encoding for time series and its application in stock trend prediction , 2021, Knowl. Based Syst..

[11]  Qingling Duan,et al.  Application of an adaptive PID controller enhanced by a differential evolution algorithm for precise control of dissolved oxygen in recirculating aquaculture systems , 2021 .

[12]  Ke Xu,et al.  Price graphs: Utilizing the structural information of financial time series for stock prediction , 2021, Inf. Sci..

[13]  Moo-Hyun Kim,et al.  Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades , 2021 .

[14]  Guancen Lin,et al.  Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting , 2021, Expert Syst. Appl..

[15]  Ligang Cui,et al.  BBO-BPNN and AMPSO-BPNN for multiple-criteria inventory classification , 2021, Expert Syst. Appl..

[16]  Yuanjiang Li,et al.  Clothing Sale Forecasting by a Composite GRU–Prophet Model With an Attention Mechanism , 2021, IEEE Transactions on Industrial Informatics.

[17]  Yicong Zhou,et al.  PID Controller-Guided Attention Neural Network Learning for Fast and Effective Real Photographs Denoising , 2021, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Jianping Wang,et al.  Prediction of dissolved oxygen in pond culture water based on K-means clustering and gated recurrent unit neural network , 2020 .

[19]  Huimin Zhao,et al.  A Novel Gate Resource Allocation Method Using Improved PSO-Based QEA , 2020, IEEE Transactions on Intelligent Transportation Systems.

[20]  Sinan Q. Salih,et al.  Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination , 2020 .

[21]  Bo Chen,et al.  Prediction of dissolved oxygen in aquaculture based on gradient boosting decision tree and long short-term memory network: A study of Chang Zhou fishery demonstration base, China , 2020, Comput. Electron. Agric..

[22]  Brian R. King,et al.  Time series prediction using deep echo state networks , 2020, Neural Computing and Applications.

[23]  J. Adamowski,et al.  Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model , 2020, Stochastic Environmental Research and Risk Assessment.

[24]  Qian Zhang,et al.  Attention-based recurrent neural networks for accurate short-term and long-term dissolved oxygen prediction , 2019, Comput. Electron. Agric..

[25]  Haoyong Yu,et al.  Efficient PID Tracking Control of Robotic Manipulators Driven by Compliant Actuators , 2019, IEEE Transactions on Control Systems Technology.

[26]  Guangyan Huang,et al.  Prediction of dissolved oxygen content in aquaculture using Clustering-based Softplus Extreme Learning Machine , 2019, Comput. Electron. Agric..

[27]  Jian Wang,et al.  Construction of traffic state vector using mutual information for short-term traffic flow prediction , 2018, Transportation Research Part C: Emerging Technologies.

[28]  Nicholas Cummins,et al.  Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning. , 2018, Methods.

[29]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Deep learning for biological image classification , 2017, Expert Syst. Appl..

[30]  Tamim Asfour,et al.  Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks , 2017, Robotics Auton. Syst..

[31]  Daoliang Li,et al.  A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture , 2014, Eng. Appl. Artif. Intell..

[32]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[33]  Ahmad B. Rad,et al.  Self-tuning PID controller using Newton-Raphson search method , 1997, IEEE Trans. Ind. Electron..

[34]  Ranjan Kumar Behera,et al.  Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data , 2021, Inf. Process. Manag..