Experimental Study of Learning Capability for Parallel-type Neuron Network(<Special Issue>COMPUTATIONAL INTELLIGENCE)

• KOBAYAKAWA Shunsuke

  Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology

• YOKOI Hirokazu

  Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology

抄録

Parallel-type neuron network (PNN) is researched to improve on decreases in capabili-ties of a neuron network by the interference of learning caused between outputs of two or more outputs BP network (BPN) and the difficulty for common achievement of middle layers used.for its each output. Research to compare prediction accuracies of nonlinear time series signals prediction systems using BPN and PNN has been performed so far. However, it has not attained demonstrating an existence of dominance concerning all prediction accuracies of PNN to BPN. Then, experimental evaluation for the dominance concerning all outputs of PNN which exists theoretically by results of comparison concerning learning rules of BPN and PNN was performed using nonlinear time series signals prediction systems in this research. As a result, the dominance was shown.

収録刊行物

• International Journal of Biomedical Soft Computing and Human Sciences: the official journal of the Biomedical Fuzzy Systems Association

  International Journal of Biomedical Soft Computing and Human Sciences: the official journal of the Biomedical Fuzzy Systems Association 16 (2), 29-37, 2011

  バイオメディカル・ファジィ・システム学会

キーワード

• BP network
• Learning rule
• Nonlinear time series signals prediction system
• Parallel-type neuron network
• Prediction accuracy

詳細情報

• CRID: 1390001206078106496
• NII論文ID: 110009717810
• DOI: 10.24466/ijbschs.16.2_29
• ISSN: 2424256X
• ISSN: 21852421
• 本文言語コード: en
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可