| Citation: | LI L L，ZHANG Z H，ZHANG Y D. Multi-input Fourier neural network and its sparrow search optimization[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：623-633 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0404
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In engineering applications, the back-propagation (BP) neural network often encounters many limitations due to its slow convergence and high noise sensitivity, and the reported Fourier neural networks cannot extract the features of multi-attribute input data. Hereby, this work proposes a gradient descent-based multi-input Fourier neural network after integrating the multi-layer perceptron with an overlapping Fourier neural network. Then to address the difficulty in deciding the global optimal parameter settings, the Cat chaotic map and the mechanisms of population-size adjustment and parameter adaptiveness are designed to promote the sparrow search algorithm’s ability to balance global exploration and local exploitation. An improved sparrow search algorithm is thus developed, optimizing the parameter settings and solving high dimensional function optimization problems. The theoretical analysis shows that the improved algorithm’s computational complexity is decided by its population size and the optimization problem dimension. Numerically comparative experiments have validated that the acquired Fourier neural network can effectively extract the features of multi-attribute data with strong generalization ability, and that the improved algorithm has significant advantages in coping with high dimensional function optimization problems.
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