Optimizing Neural Network Performance through Adaptive Learning Rate Strategies in Real-Time Systems

Abstract

In recent years, neural networks have seen widespread adoption across various real-time system applications, from autonomous driving to medical diagnostics. This paper presents a novel approach to improving the performance of neural networks by implementing adaptive learning rate strategies. The proposed method dynamically adjusts the learning rate during training based on system performance metrics, ensuring faster convergence while maintaining stability. Experimental results demonstrate a significant reduction in training time and improved model accuracy across diverse datasets. This study provides a comprehensive evaluation of different learning rate adjustment techniques and highlights their potential to enhance the efficiency of real-time neural network applications.