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首页> 外文期刊>IEEE Transactions on Neural Networks >Planning with a functional neural network architecture
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Planning with a functional neural network architecture

机译：使用功能性神经网络架构进行规划

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摘要

Introduces the concept of planning in an interactive environment between two systems: the challenger and the responder. The responder's task is to produce behavior that relates to the challenger's behavior through some response function. In this setup, we concentrate planning on the responder's actions and use the produced plan in order to control the responder. In general, the responder is assumed to be a nonlinear system whose input-output (I/O) map may be expressed by a Volterra series. The planner uses an estimate of the challenger's future output sequence, the response function, and a model of the responder's I/O relation implemented through a functional artificial neural network (FANN) architecture, in order to produce the input sequence that will be applied to the responder in the future, in parallel-time with the challenger's corresponding output sequence. The responder accepts input from the planner, which may be combined with feedback information, in order to produce an output sequence that relates to the challenger's output sequence according to the response function. The importance of planning for the generation of smooth behavior is discussed, and the effectiveness of the planner's implementation using neural network technology is demonstrated with an example.
机译：引入了在两个系统（挑战者和响应者）之间的交互环境中进行计划的概念。响应者的任务是通过某些响应功能产生与挑战者的行为相关的行为。在此设置中，我们将计划重点放在响应者的动作上，并使用生成的计划来控制响应者。通常，将响应器假定为非线性系统，其输入输出（I / O）映射可由Volterra级数表示。计划者使用对挑战者未来输出序列的估计，响应函数以及通过功能人工神经网络（FANN）架构实现的响应者I / O关系模型，以生成将应用于以下方面的输入序列：将来与响应者相应的输出顺序并行响应者。响应者接受来自计划者的输入，该输入可以与反馈信息相结合，以便根据响应功能产生与挑战者的输出序列有关的输出序列。讨论了为生成平滑行为而进行计划的重要性，并通过示例演示了使用神经网络技术实施计划程序的有效性。

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