This paper presents a new neurodynamic approach for solving the constrained pseudoconvex optimization problem based on more general assumptions. The proposed neural network is equipped with a hard comparator function and a piecewise linear function, which make the state solution not only stay in the feasible region, but also converge to an optimal solution of the constrained pseudoconvex optimization problem. Compared with other related existing conclusions, the neurodynamic approach here enjoys global convergence and lower dimension of the solution space. Moreover, the neurodynamic approach does not depend on some additional assumptions, such as the feasible region is bounded, the objective function is lower bounded over the feasible region or the objective function is coercive. Finally, both numerical illustrations and simulation results in support vector regression problem show the well performance and the viability of the proposed neurodynamic approach.

中文翻译：

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求解非光滑约束伪凸优化问题的神经动力学方法。

本文基于更一般的假设，提出了一种新的神经动力学方法来解决约束伪凸优化问题。所提出的神经网络具有硬比较器函数和分段线性函数，使得状态解不仅停留在可行区域内，而且收敛到约束伪凸优化问题的最优解。与其他相关现有结论相比，此处的神经动力学方法具有全局收敛性和较小的解空间维。此外，神经动力学方法不依赖于某些其他假设，例如，可行区域是有界的，目标函数在可行区域上是下界的，或者目标函数是强制性的。最后，