Pulse density neural network system using simultaneous perturbation learning rule
説明
Learning scheme is very important in implementation of neural networks to take advantage of their learning ability. Usually, the back-propagation method is widely used as a learning rule of neural networks. Since the backpropagation needs error back propagation to update weights, realizing it in a form of hardware is relatively difficult. In this paper, we present a pulse density neural network system with learning ability. As learning rules, the simultaneous perturbation method is used. The learning rules need only one forward operation of networks. Thus, without a complicated circuit to calculate gradients of an error function, we could construct the network system with learning ability. Pulse density is used to represent basic quantities in this system. A result for the exclusive OR problem is shown.
収録刊行物
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- Proceedings of International Conference on Neural Networks (ICNN'97)
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Proceedings of International Conference on Neural Networks (ICNN'97) 2 980-984, 2002-11-22
IEEE