Abstract
This paper presents an efficient learning algorithm for complex-valued feedforward neural networks with application to classification problems. It simplifies complex-valued neural networks learning by using the forward-only computation rather than traditional forward and backward computations. By incorporating the forward-only computation, the complex-valued Levenberg-Marquardt algorithm becomes more efficient. Comparison results of computation cost show that the proposed forward-only complex-valued learning algorithm can be faster than the traditional implementation of the Levenberg-Marquardt algorithm.
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Acknowledgements
This work was jointly supported by the National Natural Science Foundation of China under Grant nos. 61273122 and 61005047, and the Qing Lan Project of Jiangsu Province. This publication was made possible by NPRP grant: NPRP 8-274-2-107 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the author[s].
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Guo, W., Huang, H., Huang, T. (2017). Complex-Valued Feedforward Neural Networks Learning Without Backpropagation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_11
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DOI: https://doi.org/10.1007/978-3-319-70093-9_11
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