Adaptive Neural Networks for Dynamic Data Stream Analysis in Real-Time Systems

Abstract

The rapid expansion of data generation in real-time systems necessitates advanced analytical frameworks capable of processing dynamic data streams efficiently. This paper proposes an Adaptive Neural Network (ANN) architecture designed to enhance the analysis of continuously evolving datasets in real-time environments. By integrating self-adjusting mechanisms, the proposed ANN adapts its structure and parameters in response to changing data distributions and patterns. This adaptability is achieved through an innovative learning algorithm that balances exploration and exploitation, allowing the network to dynamically adjust to new information while maintaining performance on previously learned tasks. We evaluate the proposed ANN against traditional static models using a series of benchmark datasets, demonstrating significant improvements in predictive accuracy and computational efficiency. The experimental results indicate that our adaptive approach reduces latency and increases robustness, making it particularly suitable for applications in finance, healthcare, and smart cities, where real-time decision-making is critical. This work contributes to the growing body of knowledge on adaptive systems and highlights the potential of neural networks in managing the complexities of dynamic data streams.