A Hierarchical Learning System Incorporating with Supervised, Unsupervised and Reinforcement Learning

  • Jinglu Hu
  • Takafumi Sasakawa
  • Kotaro Hirasawa
  • Huiru Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4491)

Abstract

According to Hebb’s Cell assembly theory, the brain has the capability of function localization. On the other hand, it is suggested that the brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. Inspired by the above knowledge of brain, we present a hierarchical learning system consisting of three parts: supervised learning (SL) part, unsupervised learning (UL) part and reinforcement learning (RL) part. The SL part is a main part learning input-output mapping; the UL part realizes the function localization of learning system by controlling firing strength of neurons in SL part based on input patterns; the RL part optimizes system performance by adjusting parameters in UL part. Simulation results confirm the effectiveness of the proposed hierarchical learning system.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jinglu Hu
    • 1
  • Takafumi Sasakawa
    • 1
  • Kotaro Hirasawa
    • 1
  • Huiru Zheng
    • 2
  1. 1.Waseda University, Kitakyushu, FukuokaJapan
  2. 2.University of Ulster, N.IrelandUK

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