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DL-RNN: An Accurate Indoor Localization Method via Double RNNs
IEEE Sensors Journal, 2020Wireless fingerprinting localization method learns a mapping function from a fingerprint measurement to the estimated location, which is more suitable for complex indoor environments than the propagation model-based methods. However, most traditional methods only consider the location matching at single time or space points, but ignore the fact that ...
Siqi Bai +5 more
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Advanced RNN Based NARMA Predictors
Journal of VLSI signal processing systems for signal, image and video technology, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mandic, Danilo P., Chambers, Jonathon A.
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Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018
Failure event prediction is becoming increasingly important in wide applications, such as the planning of proactive maintenance, the active investment management, and disease surveillance. To address the issue, the hazard function in survival analysis has been employed to describe the pattern of failures.
Bin Liang +3 more
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Failure event prediction is becoming increasingly important in wide applications, such as the planning of proactive maintenance, the active investment management, and disease surveillance. To address the issue, the hazard function in survival analysis has been employed to describe the pattern of failures.
Bin Liang +3 more
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2020
In this chapter, we will introduce the typical deep neural networks from the viewpoint of CNN family, especially region-based CNN, SSD, and YOLO. Meanwhile, from the viewpoint of time series analysis, we depict the RNN family, namely, LSTM, GRU, FRU, etc.
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In this chapter, we will introduce the typical deep neural networks from the viewpoint of CNN family, especially region-based CNN, SSD, and YOLO. Meanwhile, from the viewpoint of time series analysis, we depict the RNN family, namely, LSTM, GRU, FRU, etc.
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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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Interval-Valued Reduced RNN for Fault Detection and Diagnosis for Wind Energy Conversion Systems
IEEE Sensors Journal, 2022Majdi Mansouri +2 more
exaly