H∞ bounds for quasi-Newton adaptive algorithm

The upper and lower bounds for the H∞ norm of the quasi-Newton (QN) family of adaptive filtering algorithms are obtained. A simulation study of the behaviour of the bounds with respect to the iteration order and the weight-vector dimension for a practical input data model reveals that the QN family is quite robust against modelling errors and errors due to the uncertainty in the initial weight-vector assumption.