Broad Bayesian learning (BBL), a novel probabilistic Bayesian neural network methodology with optimized architecture configuration, is proposed. It has an expandable feedforward broad learning network. Therefore, the uncertain estimates can be quantified in terms of probability distributions and network architecture augmentation can be adopted incrementally by use of the inherited information from the previously trained network. Furthermore, a learning network architecture configuration optimization scheme is proposed to determine the optimal architecture configuration. Based on the plausibilities of the concerned configurations, the most plausible one can be obtained, and it indicates the proper augmentation to develop the optimal configuration. To demonstrate the proposed methodology, three simulation examples and an application with in-field structural health monitoring measurement are presented.

中文翻译：

---

用于具有优化架构配置的非参数概率建模的广泛贝叶斯学习 (BBL)

广泛贝叶斯学习 (BBL) 是一种具有优化架构配置的新型概率贝叶斯神经网络方法。它有一个可扩展的前馈广泛的学习网络。因此，不确定估计可以根据概率分布进行量化，并且可以通过使用来自先前训练网络的继承信息逐步采用网络架构增强。此外，提出了一种学习网络架构配置优化方案，以确定最优架构配置。根据相关配置的合理性，可以获得最合理的配置，并指示适当的扩充以开发最佳配置。为了证明提议的方法，