<ul data-eligibleForWebStory="true"><li>A new approach, gradient-based neuroplastic adaptation, is proposed for optimizing Neuro-fuzzy networks (NFNs) parameters and structure concurrently.</li><li>NFNs are symbolic function approximations with advantages like transparency and universal function approximation ability.</li><li>The traditional sequential design process for NFNs is inefficient, leading to suboptimal architecture; the new approach addresses this limitation.</li><li>Empirical evidence shows the effectiveness of the new method in training NFNs with online reinforcement learning to excel in vision-based video game scenarios.</li></ul>

Gradient-Based Neuroplastic Adaptation for Concurrent Optimization of Neuro-Fuzzy Networks

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