A Hybrid Fuzzy Neural Network Model for Time Series Prediction in Complex Systems - Connor Redford; Ian Hollis (Computational Intelligence Systems, 2026)
The predictive modeling of time series within complex socio-technical systems remains a fundamental challenge for modern engineering, primarily due to the inherent non-linearity, stochastic volatility, and linguistic ambiguity characteristic of large-scale infrastructures. While traditional deep learning architectures provide high-dimensional mapping capabilities, they often lack the linguistic interpretability and uncertainty handling required for robust decision-making in critical sectors. This research proposes and analyzes a systemic architecture for a Hybrid Fuzzy Neural Network (HFNN) model specifically designed for time series prediction in environments where data precision is compromised by systemic noise or conceptual vagueness. By integrating the rule-based transparency of fuzzy logic with the adaptive learning potential of neural networks, the HFNN addresses the structural trade-offs between predictive accuracy and cognitive interpretability. This paper provides an extensive analytical discussion on the system-level integration of these paradigms, focusing on the architectural governance of hybrid models, the sustainability of high-compute training in decentralized infrastructures, and the policy implications of deploying such models in automated governance frameworks. We explore the socio-technical dimensions of model robustness, particularly in the context of fairness and algorithmic accountability. The findings suggest that a hybridized approach not only enhances the stability of predictions in volatile systems like energy grids and global logistics but also provides a more resilient foundation for the long-term governance of intelligent infrastructures.