Comparative Study of Classical Expert Systems and Deep Neural Networks in Complex Pattern Recognition Tasks
Keywords:
Expert Systems, Deep Neural Networks, Pattern Recognition, Artificial Intelligence, Knowledge Representation, Machine LearningAbstract
This study presents a comparative analysis of classical expert systems and deep neural networks (DNNs) in the domain of complex pattern recognition. Expert systems, grounded in rule-based inference, have historically provided interpretable decision-making in constrained environments. In contrast, DNNs demonstrate remarkable scalability and adaptability to large, high-dimensional datasets, albeit with challenges in transparency and computational demands. The paper explores their methodological foundations, applications, and limitations, integrating evidence from prior studies to highlight their respective strengths. The findings suggest that while expert systems retain value in domains requiring explainability and domain knowledge encoding, DNNs outperform in data-intensive, nonlinear, and dynamic contexts. This comparative exploration provides a framework for aligning the selection of intelligent systems with specific application requirements.
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