A metric for quantifying nonlinearity in k-dimensional complex-valued functions
Larry C Llewellyn,
Michael R Grimaila,
Douglas D Hodson and
Scott Graham
The Journal of Defense Modeling and Simulation, 2024, vol. 21, issue 1, 5-15
Abstract:
Modeling and simulation is a proven cost-efficient means for studying the behavioral dynamics of modern systems of systems. Our research is focused on evaluating the ability of neural networks to approximate multivariate, nonlinear, complex-valued functions. In order to evaluate the accuracy and performance of neural network approximations as a function of nonlinearity (NL), it is required to quantify the amount of NL present in the complex-valued function. In this paper, we introduce a metric for quantifying NL in multi-dimensional complex-valued functions. The metric is an extension of a real-valued NL metric into the k -dimensional complex domain. The metric is flexible as it uses discrete input–output data pairs instead of requiring closed-form continuous representations for calculating the NL of a function. The metric is calculated by generating a best-fit, least-squares solution (LSS) linear k -dimensional hyperplane for the function; calculating the L2 norm of the difference between the hyperplane and the function being evaluated; and scaling the result to yield a value between zero and one. The metric is easy to understand, generalizable to multiple dimensions, and has the added benefit that it does not require a closed-form continuous representation of the function being evaluated.
Keywords: Nonlinearity metric; complex-valued functions; modeling; simulation (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/15485129221080399 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sae:joudef:v:21:y:2024:i:1:p:5-15
DOI: 10.1177/15485129221080399
Access Statistics for this article
More articles in The Journal of Defense Modeling and Simulation
Bibliographic data for series maintained by SAGE Publications ().