Discretization of analog communication signals by noise addition in reinforcement learning of communication
Discretization of analog communication signals by noise addition in reinforcement learning of communication
复制标题
通信强化学习中通过噪声添加实现模拟通信信号的离散化
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
K. Shibata
中科院分区：
文献类型：
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作者：
K. Shibata
对于神经网络对符号和模式的统一处理，研究了仅通过强化学习训练的神经网络产生符号。假设了一个非常简单的交流学习任务，并在交流信号中加入了一些噪声。学习后，随着学习过程中噪声级的增大，通信信号被二值化得更多，除非噪声级过大，否则系统对噪声的容忍度也会提高。接收器也试图将信号解释为二值化的值。进一步研究了递归神经网络对离散化的促进作用。
Towards the unified processing of symbols and patterns by neural networks, it was examined that symbols emerge using neural networks that is trained only by reinforcement learning. A very simple communication-learning task was assumed, and some noise is added to the communication signals. After learning, as the noise level during learning became larger, the communication signals were binarized more, and the system became more tolerant of noise unless the noise level was too large. The receiver was also trying to interpret the signals as binarized value. Furthermore, it was examined that recurrent neural networks promote the discretization.
通信学习中通过加噪实现模拟通信信号的离散化
DOI：
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发表时间：
2004
期刊：
Proc. of The 9th AROB (Int'l Sympo. on Artificial Life and Robotics) Vol. 2
影响因子：
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作者：
K.Shibata;M.Nakanishi
通讯作者：
M.Nakanishi