In this paper

we propose an Equal-Error Range Approximation and Shrinking Learning Algorithm for multilayer perceptrons. It requires the error between each network output node activation and its target to fall into a given error range

thus it can learn faster in lower calculation cost and may avoid reversed target output and overlearning. hence itcan improve networks’generalization abilities in pattern recognitions. Through gradually Shrinking of the error range

it can also enable the networks to learn the targets more accurately in less training iterations. Finally

we apply this learning algorithm trained network to the EEG detection

and the experiment results have showed the above advantages of the proposed algorithm.