Fundamentals of higher order neural networks for modeling and simulation

Madan M. Gupta, Ivo Bukovsky, Noriyasu Homma, Ashu M.G. Solo, Zeng Guang Hou

Research output: Chapter in Book/Report/Conference proceedingChapter

26 Citations (Scopus)

Abstract

In this chapter, the authors provide fundamental principles of Higher Order Neural Units (HONUs) and Higher Order Neural Networks (HONNs) for modeling and simulation. An essential core of HONNs can be found in higher order weighted combinations or correlations between the input variables and HONU. Except for the high quality of nonlinear approximation of static HONUs, the capability of dynamic HONUs for the modeling of dynamic systems is shown and compared to conventional recurrent neural networks when a practical learning algorithm is used. In addition, the potential of continuous dynamic HONUs to approximate high dynamic order systems is discussed, as adaptable time delays can be implemented. By using some typical examples, this chapter describes how and why higher order combinations or correlations can be effective for modeling of systems.

Original languageEnglish
Title of host publicationArtificial Higher Order Neural Networks for Modeling and Simulation
PublisherIGI Global
Pages103-133
Number of pages31
ISBN (Print)9781466621756
DOIs
Publication statusPublished - 2012 Dec 1

ASJC Scopus subject areas

  • Computer Science(all)

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