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Radial Basis Function Networks and their Application in Communication Systems

In: Computational Intelligence

Author

Listed:
  • Ascensiòn Gallardo Antolìn

    (EPS-Universidad Carlos III de Madrid, Avda. de la Universidad, Departamento de Teorìa de la Senal y Comunicaciones)

  • Juan Pascual Garcìa

    (Universidad Politècnica de Cartagena Campus de la Muralla del Mar, Departamento de las Tecnologìas de la Informaciòn y las Comunicaciones)

  • Josè Luis Sancho Gòmez

    (Universidad Politècnica de Cartagena Campus de la Muralla del Mar, Departamento de las Tecnologìas de la Informaciòn y las Comunicaciones)

Abstract

Among the different types of Neural Networks (NN), the most popular and frequently used architectures are the Multilayer Perceptron (MLP) and Radial Basis Functions (RBF) due to their approximation capabilities. In this chapter we discuss the use of RBF networks to solve problems in different areas related to communications systems. In the first part of the chapter, we revise the structure of the RBF networks and the main procedures to train them. In the second part, the main applications of RBF networks in communication systems are presented and described. In particular, we will focus our attention in antenna array signal processing (direction-of-arrival estimation and beamforming) and channel equalization (intersymbol interferences and co-channel interferences). Other applications such as coding/decoding, system identification, fault detection in access networks and automatic recognition of wireless standards are also mentioned

Suggested Citation

  • Ascensiòn Gallardo Antolìn & Juan Pascual Garcìa & Josè Luis Sancho Gòmez, 2007. "Radial Basis Function Networks and their Application in Communication Systems," Springer Books, in: Diego Andina & Duc Truong Pham (ed.), Computational Intelligence, chapter 0, pages 109-130, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-37452-9_5
    DOI: 10.1007/0-387-37452-3_5
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