Convergence Analysis of Recurrent Neural Network with Self-feedback loops

3 Citations 9 References

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Other Title
  • 結合行列の固有値に基づく自己フィードバックループを持つ相互結合形ニューラルネットワークの収束性の解析

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Description

Recurrent Neural Networks (RNN) with self-feedback loops can find the solution of a combinatorial optimization problem with less trial than that with no loop. However, the theoretical analysis of this type of network have not been sufficient. In this paper, the convergence property of the RNNs with feedback loops is analyzed. For this purpose, the eigenvalue analysis and the stability analysis are used. The eigenvalues of the connection weight matrix yield the macroscopic information about network dynamics. The stability of the network state space gives the microscopic information about the convergence to the vertecies. From these analyses, it is conformed that the convergence property to the quasi-optimal solution is improved by increasing the coefficients for self-feedback loops, which correspond to the diagonal elements of the connection weight matrix. However, it is also conformed that since the quasi-optimal solution is essentially different from the least energy solution, RNNs do not always gurantee the optimal solution.

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Details 詳細情報について

  • CRID
    1571698602422964864
  • NII Article ID
    110003233057
  • NII Book ID
    AN10091178
  • Text Lang
    ja
  • Data Source
    • CiNii Articles

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