A new system identification technique using Kaiman filtering and multilayer neural networks

Abstract

The objective of this work is to use the back-propagation algorithm in conjunction with Kaiman filtering in order to establish a new self-learning technique of multilayer neural network (MNN). This new technique is developed by directly building a Kaiman filtering model for each perceptron in order to increase the adaptability of the MNN and to provide for on-line nonlinear system identification. We demonstrate that this new technique is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. We also find that it is less sensitive to the initial weights and to the learning parameters.