This paper introduces a novel collaborative neurodynamic model for computing nonnegative Canonical Polyadic Decomposition (CPD). The model relies on a system of recurrent neural networks to solve the underlying nonconvex optimization problem associated with nonnegative CPD. Additionally, a discrete-time version of the continuous neural network is developed. To enhance the chances of reaching a potential global minimum, the recurrent neural networks are allowed to communicate and exchange information through particle swarm optimization (PSO). Convergence and stability analyses of both the continuous and discrete neurodynamic models are thoroughly examined. Experimental evaluations are conducted on random and real-world datasets to demonstrate the effectiveness of the proposed approach.

翻译：本文提出了一种新颖的协作神经动力学模型，用于计算非负规范多分量分解。该模型依赖一个递归神经网络系统来求解与非负CPD相关的底层非凸优化问题。此外，还开发了连续神经网络的离散时间版本。为了增加达到潜在全局最小值的可能性，允许递归神经网络通过粒子群优化算法进行通信和信息交换。本文深入研究了连续和离散神经动力学模型的收敛性与稳定性分析。在随机数据集和真实世界数据集上进行了实验评估，以证明所提方法的有效性。