Integrator neurons for analog neural networks
Integrator neurons for analog neural networks
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用于模拟神经网络的积分器神经元
DOI：
10.1109/31.55052
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发表时间：
1990
期刊：
影响因子：
--
通讯作者：
Y. Sawada
中科院分区：
文献类型：
--
作者：
H. Yanai;Y. Sawada
结果表明，饱和积分器可以作为模拟神经网络的神经元。一个非增势函数被定义为网络。计算机模拟表明，神经网络在较宽的参数范围内工作良好。因此，可以选择合理的参数，例如，如果允许处理时间的变化，则可以避免一定频率范围的噪声的影响而不降低性能;对于由放大器构造的神经网络，情况并非如此。讨论了两种网络性能不同的原因。>
It is shown that integrators with saturation can be used as neurons for analog neural networks. A nonincreasing potential function is defined for the network. Computer simulations show that the neural network works well in wider parameter regions. Therefore, it is possible to choose reasonable parameters, for example, to avoid influence of noise of a certain frequency range without degrading performance, if changes are allowed in processing time; this is not the case for neural networks constructed from amplifiers. The reason for the different performances of the two networks is discussed. >