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2018
This chapter will discuss the concepts of recurrent neural networks (RNNs) and their modified version, long short-term memory (LSTM). LSTM is mainly used for sequence prediction. You will learn about the varieties of sequence prediction and then learn how to do time-series forecasting with the help of the LSTM model.
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This chapter will discuss the concepts of recurrent neural networks (RNNs) and their modified version, long short-term memory (LSTM). LSTM is mainly used for sequence prediction. You will learn about the varieties of sequence prediction and then learn how to do time-series forecasting with the help of the LSTM model.
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Search RNN on Broadcast Environment
Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), 2007While the RNN is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcasting environment. The liner property of wireless broadcast media and power conserving requirement of mobile devices make the problem particularly interesting and challenging. In this paper, the issues involved with
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NGCU: A New RNN Model for Time-Series Data Prediction
Big Data Research, 2022Jingyang Wang +2 more
exaly
Interval-Valued Reduced RNN for Fault Detection and Diagnosis for Wind Energy Conversion Systems
IEEE Sensors Journal, 2022Majdi Mansouri +2 more
exaly
RNN-LSTM-Based Model Predictive Control for a Corn-to-Sugar Process
Processes, 2023Chengbo Li, Yachao Dong, Jian Du
exaly
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network
Physica D: Nonlinear Phenomena, 2020Alex Sherstinsky
exaly