Marcelino Martinez_Sober⁽¹⁾, EMILIO SORIA OLIVAS⁽²⁾, Antonio J. Serrano López⁽³⁾, Alfredo Rosado⁽⁴⁾, José David Martín Guerrero^(5)
(1)Universidad de Valencia - Burjassot. Spain
⁽²⁾DPTO INGENIERÍA ELECTRONICA. FACULTAD DE FISICAS - BURJASSOT/VALENCIA. Spain
⁽³⁾Dpto. Electrónica. Universidad de Valencia - Burjasot. Spain
⁽⁴⁾Departamento de Electrónica. Universidad de Valencia - Burjassot. Spain
⁽⁵⁾G.P.D.S. Departament d´Enginyeria Electrònica. Universitat de València - Burjassot. Spain

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INTRODUCTION

Most adaptive systems currently available are based on FIR filters due to its simplicity and stability. Nevertheless, these systems are unable to achieve an optimal performance in applications with a heavy non-linear component. Another problem adds when these adaptive systems are applied to separate a series of elements in a given set of classes, as the linear output of adaptive systems is not the fittest for these applications. To overcome this limitation while maintaining the advantages of the FIR filters, the structure given in figure 1 is proposed, where F(y) is a non-linear function. Thus, in a system identification problem where the unknown system is non-linear, the precision of the adaptive system will increase. This approach entails a reduction in the computational burden of other solutions such as Volterra filters ⁽¹⁾ as the type of the filter remains unchanged and only its output is modified with a simple function. Another advantage of this structure is that it p

resents a much shorter learning time than neural networks, so it can be applied to real-time applications such as echo cancelling on a communication link, channel equalisation, etc. To adequately solve this type of problems, a recursive learning algorithm is also proposed, as this type of procedures are much faster than those based on the gradient.

Discussion Board

Any Comment to this presentation?

[ABSTRACT] [INTRODUCTION] [MATHEMATICAL DEVELOPMENT] [RESULTS] [IMAGES] [CONCLUSIONS] [BIBLIOGRAPHY] [Discussion Board]

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	[New Technology]	[Cardiolovascular Diseases]	[Obstetrics & Gynecology]

Marcelino Martinez_Sober, EMILIO SORIA OLIVAS, Antonio J. Serrano López, Alfredo Rosado, José David Martín Guerrero
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Last update: 17/01/00