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Generalized net model for parallel optimization of feed-forward neural network

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Abstract

The used generalized net wil give us a possibility for parallel optimization of fed-forward neural network based on assigned training pairs and time limits.

참고문헌 (10)

  1. Belis S, "FPGA Implementation of Spiking Neural Networks - an Initial Step towards Building Tangible Colaborative Autonomous Agents, FPT04, International Conference on Field-Programable Technology", pp.449~452 (2004), The University of Queensland
  2. A, "Artificial Neural Network Implementation on a Single FPGA of a Pipelined On-Line Backpropagation 13th International Symposium on System Synthesis", pp.225~229
  3. Hagan M, "Neural Network Design" (1996), PWS Publishing
  4. Krawczak M, "Generalized Net Models of Systems" (2003), Bulletin of Polish Academy of Science
  5. Maeda Y, "FPGA Implementation of a Pulse Density Neural Network With Training Ability Using Simultaneous Perturbation", 14(3) (2003)
  6. "The information displayed here is part of the FAQ monthly posted to comp", (, pp.fysisk (1994), teori[DOI]
  7. Rumelhart, "Training representation by back-propagation errors", pp.533~536 (1986)
  8. Sotirov S, "Modeling the algorithm Backpropagation for training of neural networks with generalized nets ? part 1", pp.61~67 (2003)
  9. Sotirov S, "Modeling the algorithm Backpropagation for training of neural networks with generalized nets ? part 2" (2003)
  10. Sotirov S, "A method of accelerating neural network training", 22(2), pp.163~169 (2005), Formerly Kluwer Academic Publishers B.V

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