Abstract: Based on sensitivity of node,an architecture selection for Single-hidden Layer Feed-forward Neural Networks (SLFNNs) was proposed．Beginning from an initial large number of hidden nodes,the proposed algorithm firstly employs the sensitivity to measure the significance of the hidden nodes,and then the hidden nodes are sorted in descending order by their significance,finally all unimportant nodes are pruned．The algorithm will terminate when a predefined stop condition is held．The main feasures of the proposed algorithm include the unnecessity of retraining the SLFNN,the compact architecture and the high generalizition capacity．We experimented the proposed approaches on real world datasets and UCI datasets,and the experimental results show that the proposed method is effective and efficient.

Key words: Feed-forward neural network,Architecture selection,Sensitivity,Cross-entropy