Deep Fuzzy Neural Networks: A New Paradigm for Handling Uncertainty in Autonomous Decision-Making Systems

Abstract

Autonomous decision-making systems are often challenged by uncertainty arising from dynamic environments, sensor noise, and incomplete knowledge representation. Traditional deep learning models provide high accuracy but lack interpretability and robustness under uncertain conditions. Conversely, fuzzy logic offers human-like reasoning with tolerance to ambiguity, though limited scalability reduces its deployment in complex systems. Deep Fuzzy Neural Networks (DFNNs) emerge as a hybrid paradigm, integrating the representational power of deep networks with the uncertainty-handling capabilities of fuzzy systems. This paper reviews the evolution of DFNNs, outlines their applicability in autonomous systems, and proposes conceptual architectures for robust decision-making under uncertainty. Findings highlight DFNNs as a promising framework bridging statistical learning and symbolic reasoning for next-generation intelligent autonomy.