基于EKF的神经网络自适应在线学习算法，包含例子和文档。-We show that a hierarchical Bayesian modeling approach allows us to perform

regularization in sequential learning. We identify three inference

levels within this hierarchy: model selection, parameter estimation, and

noise estimation. In environments where data arrive sequentially, techniques

such as cross validation to achieve regularization or model selection

are not possible. The Bayesian approach, with extended Kalman filtering

at the parameter estimation level, allows for regularization within

a minimum variance framework. A multilayer perceptron is used to generate

the extended Kalman filter nonlinear measurements mapping. We

describe several algorithms at the noise estimation level that allow us to

implement on-line regularization.We also show the theoretical links between

adaptive noise estimation in extended Kalman filtering, multiple

adaptive learning rates, and multiple smoothing regularization coefficients.