多输入傅里叶神经网络及其麻雀搜索优化  

Multi-input Fourier neural network and its sparrow search optimization

作　　者：黎亮亮 张著洪 张永丹 LI Liangliang;ZHANG Zhuhong;ZHANG Yongdan(Guizhou Provincial Characteristic Key Laboratory of System Optimization and Scientific Computation,College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵州省系统优化与科学计算特色重点实验室,贵阳550025

出　　处：《北京航空航天大学学报》2024年第2期623-633,共11页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金(62063002)。

摘　　要：鉴于反向传播(BP)神经网络存在灵敏度高但收敛速度慢,以及已有傅里叶神经网络不具备多输入数据特征提取能力,借助多个傅里叶神经网络构建能接收多维数据的堆叠神经网络,进而将其与多层感知器融合,获得基于梯度下降的多输入傅里叶神经网络。结合此神经网络获取全局最优参数值难的因素,通过在麻雀搜索算法中引入Cat混沌映射、动态种群规模调节机制及参数自适应调节方案,提出改进型麻雀搜索算法,并将其应用于多输入傅里叶神经网络的参数优化及高维函数优化问题的求解。理论分析可得,所提算法的计算复杂度主要由种群规模和优化问题的维度决定。比较性的数值实验表明,所获神经网络提取多源数据特征的能力和泛化能力强,同时所提算法处理高维优化问题具有明显优势且收敛速度快。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.

关 键 词：傅里叶神经网络 多层感知器 麻雀搜索 高维函数优化 多属性分类 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]