复值神经网络的准牛顿学习方法

Călin-Adrian Popa
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引用次数: 11

摘要

本文给出了复值前馈神经网络的拟牛顿学习方法的完整推导。由于这些算法在实值情况下产生了更好的训练结果,因此扩展到复值情况是提高复杂反向传播算法性能的自然选择。这些训练方法在各种众所周知的综合应用和实际应用中得到了举例说明。实验结果表明,该算法比复杂梯度下降算法有明显的改进。
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Quasi-Newton learning methods for complex-valued neural networks
This paper presents the full deduction of the quasi-Newton learning methods for complex-valued feedforward neural networks. Since these algorithms yielded better training results for the real-valued case, an extension to the complex-valued case is a natural option to enhance the performance of the complex backpropagation algorithm. The training methods are exemplified on various well-known synthetic and real-world applications. Experimental results show a significant improvement over the complex gradient descent algorithm.
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