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.