|Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control|
|Source Publication||IEEE Transaction on Industrial Informatics
|Abstract||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.|
Distributed Coordination Control
Multi-task Generative Adversarial Nets
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.
Wang JP.(2017).Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control.IEEE Transaction on Industrial Informatics(27),1-9.
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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