Application of Two-Stage Learning on Brain-like System

Author

Zhang, Liming

Author_Institution

Dept. of Electron. Eng., Fudan Univ., Shanghai

Volume

3

fYear

2005

Firstpage

1580

Lastpage

1584

Abstract

This paper proposes a two-stage learning strategy constructed by two kinds of neural networks to simulate function of human brain. In the first stage, sequence images from environment input to a HOSM neural network. By unsupervised learning, the weights are fixed which can extract local features like vision´s receptive field. In the second stage, an improved HDR neural network is built by supervised learning. This proposed structure has been implemented on a brain-like robot. Experimental results show that the learning strategy is effective

Keywords

brain models; feature extraction; image sequences; intelligent robots; learning (artificial intelligence); neural nets; robot vision; HDR neural network; HOSM neural network; brain-like robot; hierarchical overlapping sensory mapping; human brain; local feature extraction; sequence images; two-stage learning; unsupervised learning; vision receptive field; Animals; Biological neural networks; Biological system modeling; Brain modeling; Feature extraction; Humans; Neurons; Pediatrics; Supervised learning; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Brain, 2005. ICNN&B '05. International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-9422-4

Type

conf

DOI

10.1109/ICNNB.2005.1614934

Filename

1614934