A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities

As a general and rigid mathematical tool, wavelet theory has found many applications and is constantly developing. This article reviews the development history of wavelet theory, from the construction method to the discussion of wavelet properties. Then it focuses on the design and expansion of wavelet transform. The main models and algorithms of wavelet transform are discussed. The construction of rational wavelet transform (RWT) is provided by examples emphasizing the advantages of RWT over traditional wavelet transform through a review of the literature. The combination of wavelet theory and neural networks is one of the key points of the review. The review covers the evolution of Wavelet Neural Network (WNN), the system architecture and algorithm implementation. The review of the literature indicates the advantages and a clear trend of fast development in WNN that can be combined with existing neural network algorithms. This article also introduces the categories of wavelet-based applications. The advantages of wavelet analysis are summarized in terms of application scenarios with a comparison of results. Through the review, new research challenges and gaps have been clarified, which will serve as a guide for potential wavelet-based applications and new system designs.

Automated summary

As a mathematical tool, wavelet theory has various applications and is constantly evolving. This article reviews the development history of wavelet theory and focuses on the design and expansion of wavelet transform. It also discusses the advantages of rational wavelet transform and the combination of wavelet theory and neural networks. The article introduces the categories of wavelet-based applications and summarizes the advantages of wavelet analysis in different scenarios. The review clarifies new research challenges and provides guidance for potential wavelet-based applications and new system designs.