Adaptive Accelerated Gradient Algorithm for Training Fully Complex-Valued Dendritic Neuron Model

This paper presents an adaptive complex-valued Nesterov accelerated gradient (ACNAG) algorithm for the training of fully complex-valued dendritic neural model (FCVDNM). Firstly, based on the complex-valued Nesterov accelerated gradient (CNAG) algorithm, an adaptive stepsize update method is introduced by using local curvature information. Secondly, the obtained adaptive stepsize is further constrained by scaling the Malitsky-Mishchenko criterion. Experimental results demonstrate the superior convergence and efficiency of the proposed algorithm compared to CNAG for the training of FCVDNM.