Simultaneous perturbation learning rule for recurrent neural networks and its FPGA implementation
Simultaneous perturbation learning rule for recurrent neural networks and its FPGA implementation
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DOI：
10.1109/tnn.2005.852237
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发表时间：
2005-11
影响因子：
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通讯作者：
Y. Maeda;M. Wakamura
中科院分区：
文献类型：
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作者：
Y. Maeda;M. Wakamura
复发性神经网络具有有趣的属性，并且可以处理动态信息处理，而与普通的前馈神经网络不同。但是，由于没有方便的学习方案，它们通常很难使用。在本文中，描述了使用同时使用扰动方法的复发神经网络的递归学习方案。解释了复发性神经网络方案的详细程序。与普通的相关学习不同，此方法适用于模拟学习和复发神经网络的振荡解决方案的学习。此外，作为复发性神经网络的典型示例，我们考虑使用现场可编程门阵列（FPGA）考虑了Hopfield神经网络的硬件实现。描述了实施的详细信息。显示了用于模拟和振荡目标的Hopfield神经网络系统的两个示例。这些结果表明，这里提出的学习方案是可行的。
Recurrent neural networks have interesting properties and can handle dynamic information processing unlike ordinary feedforward neural networks. However, they are generally difficult to use because there is no convenient learning scheme. In this paper, a recursive learning scheme for recurrent neural networks using the simultaneous perturbation method is described. The detailed procedure of the scheme for recurrent neural networks is explained. Unlike ordinary correlation learning, this method is applicable to analog learning and the learning of oscillatory solutions of recurrent neural networks. Moreover, as a typical example of recurrent neural networks, we consider the hardware implementation of Hopfield neural networks using a field-programmable gate array (FPGA). The details of the implementation are described. Two examples of a Hopfield neural network system for analog and oscillatory targets are shown. These results show that the learning scheme proposed here is feasible.