标题： 基于梯度的神经可塑性适应用于神经模糊网络的并行优化 显示英文标题

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

摘要： 神经模糊网络（NFNs）是透明的、符号化的和通用的函数逼近，其性能与传统神经架构相当，但它们的知识是以语言IF-THEN规则的形式表达的。 尽管有这些优势，其系统设计过程仍然是一个挑战。 现有工作通常通过低效地隔离参数识别和结构识别来依次构建NFNs，导致过早地承诺脆弱且次优的架构。 我们提出了一种新颖的与应用无关的方法，称为基于梯度的神经可塑性适应，用于NFNs参数和结构的同时优化。 通过认识到NFNs的参数和结构应同时优化，因为它们深度结合，之前对NFNs来说难以接近的设置现在变得可行，例如基于视觉任务的NFNs在线强化学习。 通过在线强化学习训练NFNs以熟练地玩一个名为DOOM的基于视觉的视频游戏中的挑战场景，实证证明了同时优化NFNs的有效性。 显示英文摘要

摘要： 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.