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Simulating dynamic plastic continuous neural networks by finite elements

Joghataie, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TNNLS.2013.2294315
  3. Abstract:
  4. We introduce dynamic plastic continuous neural network (DPCNN), which is comprised of neurons distributed in a nonlinear plastic medium where wire-like connections of neural networks are replaced with the continuous medium. We use finite element method to model the dynamic phenomenon of information processing within the DPCNNs. During the training, instead of weights, the properties of the continuous material at its different locations and some properties of neurons are modified. Input and output can be vectors and/or continuous functions over lines and/or areas. Delay and feedback from neurons to themselves and from outputs occur in the DPCNNs. We model a simple form of the DPCNN where the medium is a rectangular plate of bilinear material, and the neurons continuously fire a signal, which is a function of the horizontal displacement
  5. Keywords:
  6. Finite element ; Neural networks ; Numerical modeling ; Wave propagation ; Data processing ; Finite element method ; Numerical models ; Bilinear materials ; Continuous functions ; Continuous materials ; Continuous neural networks ; Dynamic phenomena ; Horizontal displacements ; Input and outputs ; Rectangular plates ; Neurons
  7. Source: IEEE Transactions on Neural Networks and Learning Systems ; Volume 25, Issue 8 , August , 2014 , Pages 1583-1587 ; ISSN: 2162237X
  8. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6687302