发表机构
Aligarh Muslim University（阿利加穆斯林大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出时变系数神经动力学模型求解广义单调包含问题，在更宽松的单调性条件下证明固定时间收敛与鲁棒性，数值实验验证快速误差衰减。
AI 中文摘要
本文开发了时变系数神经动力学模型（TVCNDMs）来求解包含问题（IPs）$0 \in \mathcal{P}(w) + \mathcal{Q}(w)$，其中$\mathcal{P}$是极大集值算子，$\mathcal{Q}$是单值算子。底层算子被假定满足比经典单调性假设更不严格的广义单调性条件。在此框架下，建立了模型轨迹的存在性和唯一性，并证明了所提出的TVCNDM在固定时间（$\mathrm{FxT}$）内收敛到IPs的解，同时给出了沉降时间的显式估计。此外，研究了在存在有界外部扰动时TVCNDM的鲁棒性，证明了$\mathrm{FxT}$收敛性。数值模拟支持理论结果，展示了通过时变设计实现的快速误差衰减和改善的瞬态行为。
英文摘要
This manuscript develops time-varying coefficients neurodynamic models (TVCNDMs) to solve the inclusion problem (IPs) $0 \in \mathcal{P}(w) + \mathcal{Q}(w)$, where $\mathcal{P}$ is a maximal set-valued and $\mathcal{Q}$ is a single-valued operator. The underlying operators are assumed to fulfill a generalized monotonicity condition that is less restrictive than the classical monotonicity assumption. Within this framework, existence and uniqueness of the model trajectories are established, and fixed-time ($\mathrm{FxT}$) convergence of the proposed TVCNDM to the solution of the IPs, along with explicit estimates of the settling time. Furthermore, the robustness of the proposed TVCNDM is investigated in the presence of bounded external disturbances, demonstrating that the $\mathrm{FxT}$ convergence. Numerical simulations support the theoretical results, illustrating rapid error decay and improved transient behavior achieved through the time-varying design.
