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Adaptive Filtering Algorithms: New and Old

Objective of this thesis work is analyzing and comparing two different types of adaptive systems. In particular, we focus our attention on behaviour of the Echo-State Network (ESN) and the Least Mean-Square algorithm (LMS). The former is a new born kind of Recurrent Neural Network while the latter represents the most used technique concerning adaptive filtering.
Our purpose is designing a filter for prediction using such as algorithms. After introducing motivations for using adaptive systems, we will explain theories related to ESN and LMS. Afterwards, we will show how to predict a signal with these two techniques and then their performance. Finally, we will illustrate basic differences between the algorithms and that LMS presents advantages both in performance and in computational time with respect to ESN.

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ABSTRACT Objective of this thesis work is analyzing and comparing two different types of adaptive systems. In particular, we focus our attention on behaviour of the Echo-State Network (ESN) and the Least Mean-Square algorithm (LMS). The former is a new born kind of Recurrent Neural Network while the latter represents the most used technique concerning adaptive filtering. Our purpose is designing a filter for prediction using such as algorithms. After introducing motivations for using adaptive systems, we will explain theories related to ESN and LMS. Afterwards, we will show how to predict a signal with these two techniques and then their performance. Finally, we will illustrate basic differences between the algorithms and that LMS presents advantages both in performance and in computational time with respect to ESN. i

Laurea liv.II (specialistica)

Facoltà: Ingegneria

Autore: Giovanni Vecchiato Contatta »

Composta da 107 pagine.

 

Questa tesi ha raggiunto 524 click dal 16/05/2007.

 

Consultata integralmente una volta.

Disponibile in PDF, la consultazione è esclusivamente in formato digitale.