In this demo, I use the EKF and EKF with noise adaptation to train a neural network with data generated a nonlinear, non-stationary state space model. Adaptation is done by matching the innovations ensemble covariance to the covariance over time so as to make the one-step-ahead predictions become white (i.e. all the information in the data is absorbed by the model). All the derivations are presented, in detail, in Nando de Freitas, Mahesan Niranjan and Andrew Gee. Hierarchical Bayesian models for regularisation in sequential learning. Neural Computation. Vol 12 No 4, pages 955-993. After downloading the file, type tar-xf demo3.tar to uncompress it. This creates the directory demo3 containing the required m files. Go to this directory, load matlab and type ekfdemo1. Figure 1 will then show you the simulation results.

demo3\ekfdemo1.m
.....\mlpekf.m
.....\mlpekfQ.m
demo3

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