Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control
Wang JP(王军平)
2017-09
发表期刊IEEE Transaction on Industrial Informatics
期号27页码:1-9
摘要Generating sequential decision policy from huge amounts of measured process data is a novel research direction for collaborative factory automation, making full use of those online or offline process data to directly design flexible make decisions policy, and evaluate performance. The key challenges for the cross-domain sequential decision process is to online generate sequential decision making policy directly, and transferring knowledge between tasks. Most multi-task policy generating algorithms often suffer from insufficient generating cross-task sharing structure at discrete-time nonlinear systems with applications. This paper proposes the multi-task generative adversarial nets with shared memory for cross-Domain coordination control, which can generate sequential decision policy directly from raw sensory input of all of tasks, and online evaluate performance of system actions in discrete-time nonlinear systems. Experiments on three groups of discrete-time nonlinear control tasks show that our proposed model can availably improve the performance of task with the help of other related tasks.
关键词Industry 4.0 Distributed Coordination Control Multi-task Generative Adversarial Nets Decision Making
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15467
专题精密感知与控制研究中心_人工智能与机器学习
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Wang JP. Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control[J]. IEEE Transaction on Industrial Informatics,2017(27):1-9.
APA Wang JP.(2017).Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control.IEEE Transaction on Industrial Informatics(27),1-9.
MLA Wang JP."Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control".IEEE Transaction on Industrial Informatics .27(2017):1-9.
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