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首页> 外文期刊>Quality Control, Transactions >Rapidly Learning Bayesian Networks for Complex System Diagnosis: A Reinforcement Learning Directed Greedy Search Approach
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Rapidly Learning Bayesian Networks for Complex System Diagnosis: A Reinforcement Learning Directed Greedy Search Approach

机译：快速学习复杂系统诊断的贝叶斯网络：加强学习执导贪婪搜索方法

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摘要

Bayesian networks are a popular diagnosis method, whose structures are usually defined by human experts and parameters are learned from data. For the increasing complexity of modern systems, building their structures based on physical behaviors is becoming a difficult task. However, the improvement of data collection techniques motivates learning their structures from data, where greedy search is a typical iterative method. In each iteration, it generates multiple structure candidates by modifying one edge, evaluates these structures by scores based on data and selects the best structure for the next iteration. This method is costly because there are too many structures to be evaluated. To solve this problem, we frame the traditional greedy search by Markov decision process and propose an efficient Bayesian network learning approach by integrating reinforcement learning into it. In our approach, a convolutional neural network is employed as the value function to approximate scores. Before evaluating structures using data, the neural network is used to predict scores. Structure candidates with a low predicted score are discarded. By avoiding unnecessary computation, the cooperation of reinforcement learning and greedy search effectively improves the learning efficiency. Two systems, a 10-tank system with 21 monitored variables and the classic Tennessee Eastman process with 52 variables, are employed to demonstrate our approach. The experiment results indicate that the computation cost of our method was reduced by 30%similar to 50%, and the diagnosis accuracy was almost the same.
机译：贝叶斯网络是一种流行的诊断方法，其结构通常由人类专家定义，并且从数据中学习参数。为了提高现代系统的复杂性，基于身体行为构建其结构正成为一项艰巨的任务。然而，数据收集技术的改进激励从数据中学习其结构，其中贪婪搜索是典型的迭代方法。在每次迭代中，它通过修改一个边缘来生成多个结构候选，基于数据按成绩评估这些结构，并选择下一次迭代的最佳结构。这种方法昂贵，因为有太多的结构要评估。为了解决这个问题，我们通过将强化学习集成到其中，建立了马尔可夫决策过程的传统贪婪搜索，并提出了一种高效的贝叶斯网络学习方法。在我们的方法中，卷积神经网络被用作近似分数的价值函数。在使用数据评估结构之前，神经网络用于预测分数。丢弃预测得分低的结构候选者。通过避免不必要的计算，加强学习和贪婪搜索的合作有效提高了学习效率。两个系统，一个带有21个受监控变量的10箱系统和具有52个变量的经典田纳西州的Eastman流程，用于展示我们的方法。实验结果表明，我们的方法的计算成本与50％相似的30％，诊断精度几乎相同。

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