再帰型ニューラルネットワークの安定性判別のための圧縮モデルの構築と入出力特性の評価

DOI

書誌事項

タイトル別名
  • Construction and Input-to-output Characteristics Evaluation of Compressed Model of Recurrent Neural Networks for Their Stability Analysis

説明

<p>This paper proposes a model compression method of reducing the number of nonlinear activation functions of continuous-time recurrent neural networks (RNNs). Ensuring the internal stability of the compressed RNN guarantees that of the original RNN. An error bound between the outputs of the compressed RNN and the original one is derived. Moreover, an optimization problem for reducing the bound is formulated, and it is relaxed to a semi-definite programming problem. Furthermore, it is shown that the proposed model compression method produces a compressed RNN whose output is close to that of the original one as a general tendency. The proposed method is demonstrated on a simple numerical example.</p>

収録刊行物

キーワード

詳細情報 詳細情報について

  • CRID
    1390866591962855040
  • DOI
    10.9746/sicetr.61.104
  • ISSN
    18838189
    04534654
  • 本文言語コード
    ja
  • データソース種別
    • JaLC
  • 抄録ライセンスフラグ
    使用不可

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