This analysis investigates how layer variations affect prediction accuracy in artificial neural networks, suggesting implications for architectural designs.
This paper explores the influence of layer variations within Artificial Neural Network (ANN) crowds on their collective behavior and prediction accuracy. While prior research has demonstrated the effectiveness of ANN crowds, understanding how architectural variations impact performance is limited. A coding scheme is used to categorize architectures into distinct behavioral profiles (Normality, Centrality, Width). These profiles provide insights into how individual architecture contributes to the overall behavior and performance of the crowd. The research uses two prediction models. Analysis of behavior distributions across layers reveals minimal fluctuations in both models, suggesting consistent behavior across varying layer configurations. Future work will explore the relationship between layer variations and error metrics to understand their impact on performance.
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Adebayo et al. (2025) studied this question.
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