Performance Study on Complex-valued Function Approximation Problems

  • Sundaram Suresh
  • Narasimhan Sundararajan
  • Ramasamy Savitha
Part of the Studies in Computational Intelligence book series (SCI, volume 421)

Abstract

In this chapter, we evaluate the approximation performances of the fully complex valued multi-layer perceptron network and the improved fully complex-valued multi-layer perceptron network described in Chapter 2, the fully complex-valued radial basis function network and the meta-cognitive fully complex-valued radial basis function network described in Chapter 3, and the fast learning fully complex valued relaxation network described in Chapter 4. The performances of these networks are studied in comparison with existing complex-valued learning algorithms like the complex-valued extreme learning machine and the complex-valued minimal resource allocation network using two synthetic, complex-valued function approximation problems and two real-world problems. The real world problems consist of a Quadrature Amplitude Modulation (QAM) channel equalization problem with circular signals and an adaptive beam-forming problem with non-circular signals.

Keywords

Antenna Array Hide Neuron Radial Basis Function Network Quadrature Amplitude Modulation Transmitted Symbol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Sundaram Suresh
    • 1
  • Narasimhan Sundararajan
    • 2
  • Ramasamy Savitha
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.School of Electrical and Electronics EngineeringNanyang Technological UniversitySingaporeSingapore

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