Neuro-fuzzy networks (NFNs) are transparent, symbolic, and universal function approximations that perform as well as conventional neural architectures, but their knowledge is expressed as linguistic IF-THEN rules. Despite these advantages, their systematic design process remains a challenge. Existing work will often sequentially build NFNs by inefficiently isolating parametric and structural identification, leading to a premature commitment to brittle and subpar architecture. We propose a novel application-independent approach called gradient-based neuroplastic adaptation for the concurrent optimization of NFNs' parameters and structure. By recognizing that NFNs' parameters and structure should be optimized simultaneously as they are deeply conjoined, settings previously unapproachable for NFNs are now accessible, such as the online reinforcement learning of NFNs for vision-based tasks. The effectiveness of concurrently optimizing NFNs is empirically shown as it is trained by online reinforcement learning to proficiently play challenging scenarios from a vision-based video game called DOOM.

翻译：神经模糊网络（NFNs）是透明、符号化且通用的函数逼近器，其性能与常规神经架构相当，但其知识以语言化的IF-THEN规则形式表达。尽管具备这些优势，其系统化设计过程仍面临挑战。现有研究常通过低效地分离参数与结构识别来顺序构建NFNs，导致过早固化于脆弱且次优的架构。本文提出一种与具体应用无关的新方法——基于梯度的神经可塑性适应，用于并发优化NFNs的参数与结构。通过认识到NFNs的参数与结构因其深度耦合而应同步优化，该方法使得先前NFNs难以应对的场景（例如基于视觉任务的NFNs在线强化学习）成为可能。实验证明，通过在线强化学习训练NFNs在基于视觉的视频游戏DOOM中熟练应对高难度场景时，并发优化NFNs具有显著成效。