Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability

By Danilo P. Mandic, Jonathon A. Chambers. 2001

630 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/41977fee0ac98b029c6d9f6d9a6c23b584ea860d

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In Artificial Intelligence

Citations in the corpus

A review of the use of convolutional neural networks in agriculture (2018, background): “The study did not consider/include more advanced and complex models such as Recurrent Neural Networks (RNN; Mandic and Chambers, 2001) or Long Short-Term Memory (LSTM) architectures (Gers et al ., 2000).”
Recent Advances in Physical Reservoir Computing: A Review (2018, background): “Network architectures of ANNs are typically classified into feedforward networks (Schmidhuber, 2015) and recurrent networks (Mandic et al., 2001), the choice of which depends on the type of computational task.”
The use of NARX neural networks to predict chaotic time series (2008, background): “[1][12][5].”
Recurrent neural network for dynamic portfolio selection (2006, background): “The Elman network [8] is one kind of the recurrent neural networks and widely used to deal with time-varying problems [10,11].”
Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures (2020, positive): “ Thanks to the state-of-the-art advances in articial intelligence (AI), recent studies have shown that deep learning (e.g., convolutional neural network (CNN) [37] and recurrent neural network (RNN) [38,39]) is a promising approach to establish metamodels for fast prediction of time history response”
A Gentle Introduction to Deep Learning in Medical Image Processing (2018, background): “ational units generally form residual blocks, and thus variational networks are always ResNets as well. Recurrent neural networks (RNNs) enable the processing of sequences with long term dependencies [56]. Furthermore, recurrent nets introduce state variables that allow the cells to carry memory and”
Artificial Intelligence and Marketing: Pitfalls and Opportunities (2020, background): “As a solution, recurrent neural networks (Mandic & Chambers, 2020) use the output of one timestep as input for the next timestep, hence allowing the neural network to keep useful information in memory for future predictions.”
Text Classification Algorithms: A Survey (2019, methodology): “ization of the raw text data. 4.10.2. Recurrent Neural Network (RNN) Another neural network architecture that researchers have used for text mining and classiﬁcation is recurrent neural network (RNN) [178,179]. RNN assigns more weights to the previous data points of a sequence. Therefore, this techn”
Data-driven Soft Sensors in the process industry (2009, methodology): “In Menold et al. (1999) a moving window filter is combined with the Hampel identifier to obtain an outlier detection and removal system.”