We consider a model composed of nonlinear perceptrons and analytically investigate its generalization performance using correlated examples in the framework of on-line learning by a statistical mechanical method. In Hebbian and AdaTron learning, the larger the number of examples used in an update, the slower the learning. In contrast, Perceptron learning does not exhibit such behaviors, and the learning becomes fast in some time region.