Universal Approximnation and Learning of Trajectories Using Oscillators
Universal Approximnation and Learning of Trajectories Using Oscillators
复制标题
使用振荡器的轨迹的通用逼近和学习
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
K. Hornik
中科院分区：
文献类型：
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作者：
P. Baldi;K. Hornik
自然和人工神经电路必须能够遍历特定的状态空间轨迹。解决这个问题的一个自然方法是从例子中学习相关的轨迹。不幸的是，非晶态网络中复杂轨迹的梯度下降学习是不成功的。我们提出了一种可能的方法，其中轨迹是通过以各种模块化方式组合简单振荡器来实现的。我们对比了快振荡和慢振荡的两个区域。在所有情况下，我们证明了具有有界频率的振荡器组具有泛逼近性质。文中还简要讨论了尚未解决的问题。
Natural and artificial neural circuits must be capable of traversing specific state space trajectories. A natural approach to this problem is to learn the relevant trajectories from examples. Unfortunately, gradient descent learning of complex trajectories in amorphous networks is unsuccessful. We suggest a possible approach where trajectories are realized by combining simple oscillators, in various modular ways. We contrast two regimes of fast and slow oscillations. In all cases, we show that banks of oscillators with bounded frequencies have universal approximation properties. Open questions are also discussed briefly.