EVALUATING ONLINE LEARNING ALGORITHMS FOR REAL-TIME ADAPTIVE SYSTEMS

Purpose: This paper investigates the implementation and performance of online learning algorithms designed for real time adaptive systems, highlighting their mechanisms, strengths, and practical applications in dynamic environments. Design/methodology/approach: We present an analytical exploration of key online learning methods—particularly incremental, gradient based, and adaptive optimization techniques—synthesizing insights from foundational literature and experimental comparisons. Findings: Results suggest that online adaptive algorithms like adaptive gradient methods outperform simple fixed rate algorithms in dynamic environments due to their ability to adjust parameters in real time. Additionally, the trade offs between convergence speed, computational cost, and responsiveness are examined. Practical implications: Understanding algorithmic behavior in real time is critical for applications such as autonomous control, recommendation systems, and adaptive prediction models. Originality/value: This work consolidates historical developments in online learning algorithms related to real time adaptation, drawing connections between classical methods in adaptive systems and modern machine learning approaches.