Performance Study on Real-valued Classification Problems

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

Abstract

As mentioned in Chapter 5, the orthogonal decision boundaries of fully complex valued neural networks help them to perform classification tasks efficiently. Therefore, in this chapter, we study the classification performance of FC-MLP and ICMLP described in Chapter 2, FC-RBF and Mc-FCRBF explained in Chapter 3, FCRN and CC-ELM described in the chapters 5 and 6 respectively. First, the study is conducted on a set of benchmark real-valued classification problems from the UCI machine learning repository [1] and then, using a practical acoustic emission signal classification problem for health monitoring [2].

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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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