Application of fractional theory in quantum back propagation neural network. (24th May 2021)

• Record Type: Journal Article
• Authors: Dong, Yumin; Li, Xiang; Zhang, Jinlei; Li, Ziyi; Hou, Dong
• Other Names: Gao Feng guestEditor.; Feng Wenying guestEditor.; Zhang Xinguang guestEditor.; Ge Fudong guestEditor.
• Is Part Of: Mathematical methods in the applied sciences. Volume 46:Number 3(2023)
• Page Start: 3080
• Page End: 3090
• Publication Date: 2021-05-24
• Subjects: activation function -- fractional‐order theory -- fractional quantum BP neural network -- neural network; Mathematics -- Periodicals; Technology -- Mathematics -- Periodicals; 519
• DOI: 10.1002/mma.7550
• Languages: English
• ISSNs: 0170-4214

Abstract

Abstract : In this paper, by applying the theory of fractional calculus to quantum back propagation (BP) neural network, a quantum BP algorithm based on the definition of fractional Grünwald–Letnikoff (G‐L) is proposed. We choose the Sigmoid linear superposition function to replace the activation function of the traditional neural network to construct a fractional quantum BP neural network structure. Experimental results prove that this algorithm improves the convergence speed of the network and reduces the convergence error.