Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs[1][2][3] makes them applicable to tasks such as unsegmented, connected handwriting recognition[4] or speech recognition.[5][6] The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior.[7] A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled.

Additional stored states and the storage under direct control by the network can be added to both infinite-impulse and finite-impulse networks. Another network or graph can also replace the storage if that incorporates time delays or has feedback loops. Such controlled states are referred to as gated states or gated memory and are part of long short-term memory networks (LSTMs) and gated recurrent units. This is also called Feedforward Neural Network (FNN). Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs.[8]

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  2. ^ Abiodun, Oludare Isaac; Jantan, Aman; Omolara, Abiodun Esther; Dada, Kemi Victoria; Mohamed, Nachaat Abdelatif; Arshad, Humaira (2018-11-01). "State-of-the-art in artificial neural network applications: A survey". Heliyon. 4 (11): e00938. Bibcode:2018Heliy...400938A. doi:10.1016/j.heliyon.2018.e00938. ISSN 2405-8440. PMC 6260436. PMID 30519653.
  3. ^ Tealab, Ahmed (2018-12-01). "Time series forecasting using artificial neural networks methodologies: A systematic review". Future Computing and Informatics Journal. 3 (2): 334–340. doi:10.1016/j.fcij.2018.10.003. ISSN 2314-7288.
  4. ^ Graves, Alex; Liwicki, Marcus; Fernandez, Santiago; Bertolami, Roman; Bunke, Horst; Schmidhuber, Jürgen (2009). "A Novel Connectionist System for Improved Unconstrained Handwriting Recognition" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (5): 855–868. CiteSeerX 10.1.1.139.4502. doi:10.1109/tpami.2008.137. PMID 19299860. S2CID 14635907.
  5. ^ Sak, Haşim; Senior, Andrew; Beaufays, Françoise (2014). "Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling" (PDF). Google Research.
  6. ^ Li, Xiangang; Wu, Xihong (2014-10-15). "Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition". arXiv:1410.4281 [cs.CL].
  7. ^ Miljanovic, Milos (Feb–Mar 2012). "Comparative analysis of Recurrent and Finite Impulse Response Neural Networks in Time Series Prediction" (PDF). Indian Journal of Computer and Engineering. 3 (1).
  8. ^ Hyötyniemi, Heikki (1996). "Turing machines are recurrent neural networks". Proceedings of STeP '96/Publications of the Finnish Artificial Intelligence Society: 13–24.