Adaptive Space Reconstruction on Hidden Layer and Knowledge Transfer Based on Hidden-level Generalization in Layered Neural Networks
Adaptive Space Reconstruction on Hidden Layer and Knowledge Transfer Based on Hidden-level Generalization in Layered Neural Networks
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DOI：
10.9746/ve.sicetr1965.43.54
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发表时间：
2007-01
期刊：
影响因子：
--
通讯作者：
K. Shibata;Koji Ito
中科院分区：
文献类型：
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作者：
K. Shibata;Koji Ito
人类可以通过在类似的情况下采取类似的动作，在现实世界中有效地学习它的动作。为了在具有神经网络的机器人中实现这种能力，不仅要对输入信号空间进行泛化，而且要对隐含层重构空间进行基于相似性的泛化。为了解释泛化有用的隐含表示的获得，首先建立了一个假设，即当两个训练模式更接近时，对应的隐藏模式可能通过学习变得更接近。在第一个模拟中，随机输入-输出模式集的学习支持该假设。在其他以简单视觉传感器信号作为网络输入的仿真中，结果表明，隐层自适应地表示全局信息，同时保持从输入层到隐层的初始连接权值所给出的信息。最后讨论了具有视觉输入的神经网络通过对机器人到达视觉传感器捕捉到的目标的任务的强化学习来表示隐含层中的全局信息的原因。
Humans can learn its action effectively in the real world by taking a similar action in a similar situation. In order to realize such abilities in a robot with a neural network, not only generalization on the input signal space, but also the generalization based on the similarity on the reconstructed space on the hidden layer would play an important role. In this paper, to explain the acquisition of the useful hidden representation for the generalization, a hypothesis is set up at first that when two training patterns are closer, the corresponding hidden patterns are likely to become closer through learning. In the first simulation, the hypothesis is supported by the learning of random input-output pattern sets. In the other simulations where simple visual sensor signals were the input of the network, it is shown that the hidden layer represents global information adaptively while keeping the information given by the initial connection weights from the input layer to the hidden layer. Finally, the reason why a neural network with visual inputs becomes to represent the global information in the hidden layer through the reinforcement learning of the task that a robot reaches a target caught on its visual sensor, is considered.