Wavelet Neural Network

Wavelet neural networks (WNNs) combine the multi-resolution analysis capabilities of wavelet transforms with the learning power of neural networks to address challenges in processing non-stationary and multi-scale data. Current research focuses on developing novel WNN architectures, such as adaptive wavelet networks and those incorporating Swin Transformers, to improve efficiency and accuracy in diverse applications like time series forecasting, image dehazing, and signal enhancement. These advancements demonstrate the effectiveness of WNNs across various domains, offering improved performance over traditional methods and contributing to progress in fields ranging from environmental monitoring to medical image analysis. The resulting models often exhibit superior performance and efficiency compared to traditional approaches.