基于MDP的诊断策略构建方法

LIANG Yajun,XIAO Mingqing,SONG Haifang,YANG Zhao,LIANG Peng

Journal of Beijing University of Aeronautics and Astronautics（2016）

Cited 2|Views4

Abstract

针对传统方法忽略测试通过的不确定性因素,缺乏长周期寻优机制,难以在复杂测试系统中生成全局最优诊断策略的问题,提出了一种基于马尔可夫决策过程（MDP）的诊断策略构建方法。该方法将故障检测、隔离的过程表述为系统故障状态的马尔可夫过程,通过引入折扣因子与目标权重,构造了综合效用准则函数的无限折扣模型,并利用策略迭代算法求解出全局平稳最优诊断策略。实例表明,该方法充分考虑了测试通过的不确定性,可实现全局平稳策略寻优,能够有效地指导测试系统实现快速故障检测和隔离。

Key words

diagnostic strategy,Markov decision processes (MDP),fault detection,policy iteration algorithm,strategy optimization

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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