21
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Potential, challenges and future directions for deep learning in prognostics and health management applications

      , , , , ,
      Engineering Applications of Artificial Intelligence
      Elsevier BV

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references136

          • Record: found
          • Abstract: found
          • Article: not found

          Long Short-Term Memory

          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Gradient-based learning applied to document recognition

              Bookmark
              • Record: found
              • Abstract: not found
              • Conference Proceedings: not found

              Going deeper with convolutions

                Bookmark

                Author and article information

                Journal
                Engineering Applications of Artificial Intelligence
                Engineering Applications of Artificial Intelligence
                Elsevier BV
                09521976
                June 2020
                June 2020
                : 92
                : 103678
                Article
                10.1016/j.engappai.2020.103678
                767f3839-a0a2-405a-ab03-a0cd3cb5664a
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

                History

                Comments

                Comment on this article