Uma Arquitetura Híbrida Baseada em Janelamento e Redes Neurais para Projeto de Filtros FIR

This paper proposes a hybrid architecture for FIR filter design, combining windowing, neural networks, and optimization. It generates a digital weighting envelope, iteratively adjusted by a loss function, that penalizes ripple and attenuation violations. The resulting filter is generated by the product of this envelope and the ideal filter. Experimental results demonstrate the superiority of the proposed approach over the classical Parks-McClellan and Kaiser windowing methods, indicating the potential for more efficient hardware implementations with lower latency and energy consumption.