Identity Recognition Using Wavelet-Based Neural Networks: Review and Comparative Study

Wavelet neural networks are a unique combination of wavelet analysis and neural network architectures, thus providing the benefits of advanced signal processing. Unlike traditional methods, WNNs offer a multi-resolution approach that outshines for nonstationary signals; therefore, these are very useful in applications related to areas such as biomedical signal analysis, image processing, and time-series forecasting. Architecture of WNNs enables the replacement of activation functions with wavelet functions for the extraction of localized features, making both processing and interpretation of data much more efficient. Some of the recent improvements in designing WNNs improve its computational efficiency to suit the requirements of real-time applications and also to incorporate it into deep learning frameworks, but still, one of the main challenges faced in WNNs such as computational complexity and scalability need to be addressed. As the historical development, core contributions, and evolving architecture of WNNs is done, this paper represents those along with filling up certain existing research gaps. This research paper addresses future directions regarding hybrid models that combine synergies of CNNs with RNNs as well as WNNs and therefore posits opening its way for enhanced performance across various domains. Researching such an area ensures and guarantees further expansion of applications in WNNs besides strengthening their importance in signal processing in the modern arena.