Title :
Application of Two-Stage Learning on Brain-like System
Author_Institution :
Dept. of Electron. Eng., Fudan Univ., Shanghai
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;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614934
