Ricerc@Sapienza

In this paper, the problem of the online modeling of nonlinear speech signals is addressed. In particular, the goal of this work is to provide a nonlinear model yielding the best tradeoff between performance results and required computational resources. Functional link adaptive filters were proved to be an effective model for this problem, providing the best performance when trigonometric expansion is used as a nonlinear transformation.

Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system.

This chapter aims at introducing this book volume and providing the necessary guidelines for reading the contributions described in the various chapters. Therefore, we define here the principles that form the common thread between the book chapters, at the end of which the reader will have an exhaustive overview of the recent frontier issues in the research and development of learning methodologies for nonlinear modeling. For this reason, this chapter first describes what are the key concepts that will be covered in the book and deepened in each chapter.