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A brainlike learning system with supervised, unsupervised, and reinforcement learning
Author(s) -
Sasakawa Takafumi,
Hu Jinglu,
Hirasawa Kotaro
Publication year - 2007
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.20600
Subject(s) - reinforcement learning , unsupervised learning , competitive learning , artificial intelligence , computer science , reinforcement , supervised learning , linear subspace , machine learning , function (biology) , artificial neural network , psychology , mathematics , social psychology , geometry , evolutionary biology , biology
According to Hebb's cell assembly theory , the brain has the capability of function localization. On the other hand, it is suggested that in the brain there are three different learning paradigms: supervised, unsupervised, and reinforcement learning, which are related deeply to the three parts of brain: cerebellum, cerebral cortex, and basal ganglia, respectively. Inspired by the above knowledge of the brain in this paper we present a brainlike 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 is a competitive network dividing input space into subspaces and realizes the capability of function localization by controlling firing strength of neurons in the SL part based on input patterns; the RL part is a reinforcement learning scheme, which optimizes system performance by adjusting the parameters in the UL part. Numerical simulations have been carried out and the simulation results confirm the effectiveness of the proposed brainlike learning system. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(1): 32–39, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20600