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A two-layer neural network can be used to approximate any nonlinear function. T h e behavior of the hidden nodes that allows the network to do this is described. Networks with one input are analyzed first, and the analysis is then extended to networks w i t h multiple inputs. T h e result of this analysis is used to formulate a method for initialization o f the weights o f neural networks to reduce training time. Training examples are given and the learning curve for these examples are shown todoi:10.1109/ijcnn.1990.137819 dblp:conf/ijcnn/NguyenW90 fatcat:t3aw7jwua5c73brb7oc3t4gyam