Fundamentals of Higher Order Neural Networks for Modeling and Simulation-Reference-Cited by-同舟云学术

Fundamentals of Higher Order Neural Networks for Modeling and Simulation

Published:2013 Issue: Volume: Page:103-133
ISSN:
Container-title:Artificial Higher Order Neural Networks for Modeling and Simulation
language:
Short-container-title:

Author:

Gupta Madan M.¹,Bukovsky Ivo²,Homma Noriyasu³,Solo Ashu M. G.⁴,Hou Zeng-Guang⁵

Affiliation:

1. University of Saskatchewan, Canada

2. Czech Technical University in Prague, Czech Republic

3. Tohoku University, Japan

4. Maverick Technologies America Inc., USA

5. The Chinese Academy of Sciences, China

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.

Publisher

IGI Global

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