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A biologically plausible learning method for neurorobotic systems

Davoudi, H ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1109/NER.2009.5109251
  3. Publisher: 2009
  4. Abstract:
  5. This paper introduces an incremental local learning algorithm inspired by learning in neurobiological systems. This algorithm has no training phase and learns the world during operation, in a lifetime manner. It is a semi-supervised algorithm which combines soft competitive learning in input space and linear regression with recursive update in output space. This method is also robust to negative interference and compromises bias-variance dilemma. These qualities make the learning method a good nonlinear function approximator having possible applications in neuro-robotic systems. Some simulations illustrate the effectiveness of the proposed algorithm in function approximation, time-series prediction, and motor control problems. ©2009 IEEE
  6. Keywords:
  7. Function approximation ; Input space ; Learning methods ; Lifetime learning ; Local learning ; Motor control ; Neuro-robotics ; Nonlinear functions ; Recursive update ; Robotic systems ; Semi-supervised algorithm ; Soft competitive learning ; Statistical learning ; Time series prediction ; Training phase ; Approximation algorithms ; Education ; Robotics ; Robots ; Learning algorithms
  8. Source: 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09, Antalya, 29 April 2009 through 2 May 2009 ; 2009 , Pages 128-131 ; 9781424420735 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/5109251