CHAPTER 1
INTRODUCTION TO THE HILBERT
HUANG TRANSFORM
AND ITS RELATED MATHEMATICAL PROBLEMS
Norden E. Huang
The Hilbert–Huang transform (HHT) is an empirically based data-analysis
method. Its basis of expansion is adaptive, so that it can produce physically mean-
ingful representations of data from nonlinear and non-stationary processes. The
advantage of being adaptive has a price: the difficulty of laying a firm theoretical
foundation. This chapter is an introduction to the basic method, which is fol-
lowed by brief descriptions of the recent developments relating to the normalized
Hilbert transform, a confidence limit for the Hilbert spectrum, and a statistical
significance test for the intrinsic mode function (IMF). The mathematical prob-
lems associated with the HHT are then discussed. These problems include (i) the
general method of adaptive data-analysis, (ii) the identification methods of non-
linear systems, (iii) the prediction problems in nonstationary processes, which is
intimately related to the end effects in the empirical mode decomposition (EMD),
(iv) the spline problems, which center on finding the best spline implementation
for the HHT, the convergence of EMD, and two-dimensional EMD, (v) the opti-
mization problem or the best IMF selection and the uniqueness of the EMD de-
composition, (vi) the approximation problems involving the fidelity of the Hilbert
transform and the true quadrature of the data, and (vii) a list of miscellaneous
mathematical questions concerning the HHT.
1.1. Introduction
Traditional data-analysis methods are all based on linear and stationary assump-
tions. Only in recent years have new methods been introduced to analyze nonsta-
tionary and nonlinear data. For example, wavelet analysis and the Wagner-Ville
distribution (Flandrin 1999; Gr¨ochenig 2001) were designed for linear but non-
stationary data. Additionally, various nonlinear time-series-analysis methods (see,
for example, Tong 1990; Kantz and Schreiber 1997; Diks 1999) were designed for
nonlinear but stationary and deterministic systems. Unfortunately, in most real sys-
tems, either natural or even man-made ones, the data are most likely to be both
nonlinear and nonstationary. Analyzing the data from such a system is a daunting
problem. Even the universally accepted mathematical paradigm of data expansion
in terms of an aprioriestablished basis would need to be eschewed, for the con-
volution computation of the aprioribasis creates more problems than solutions.
A necessary condition to represent nonlinear and nonstationary data is to have an
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