Abstract
Conjugate gradient (CG) method has been verified to be one effective strategy for training neural networks in terms of its low memory requirements and fast convergence. In this paper, an efficient CG method is proposed to train fully complex neural networks based on Wirtinger calculus. We adopt two ways to enhance the training performance. One is to construct a sufficient descent direction during training by designing a fine tuning conjugate coefficient. Another technique is to pursue the optimal learning rate instead of a fixed constance in each iteration which is determined by employing a generalized Armijo search. To verify the effectiveness and the convergent behavior of the proposed algorithm, the illustrated simulation has been performed on the complex benchmark noncircular signal.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (No. 61305075), the Natural Science Foundation of Shandong Province (No. ZR2015A-L014, ZR201709220208) and the Fundamental Research Funds for the Central Universities (No. 15CX08011A, 18CX02036A).
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Zhang, B., Wang, J., Wu, S., Wang, J., Zhang, H. (2018). Fully Complex-Valued Wirtinger Conjugate Neural Networks with Generalized Armijo Search. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_14
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DOI: https://doi.org/10.1007/978-3-319-95957-3_14
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