In both practical applications and theoretical analysis, there are many fuzzy chance-constrained optimization problems. Currently, there is short of real-time algorithms for solving such problems. Therefore, in this paper, a continuous-time neurodynamic approach is proposed for solving a class of fuzzy chance-constrained optimization problems. Firstly, an equivalent deterministic problem with inequality constraint is discussed, and then a continuous-time neurodynamic approach is proposed. Secondly, a sufficient and necessary optimality condition of the considered optimization problem is obtained. Thirdly, the boundedness, global existence and Lyapunov stability of the state solution to the proposed approach are proved. Moreover, the convergence to the optimal solution of considered problem is studied. Finally, several experiments are provided to show the performance of proposed approach.

中文翻译：

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模糊机会约束规划的有效神经动力学方法

在实际应用和理论分析中，存在许多模糊机会约束优化问题。目前，缺乏解决此类问题的实时算法。因此，在本文中，提出了一种连续时间神经动力学方法来解决一类模糊机会约束优化问题。首先讨论了一个具有不等式约束的等价确定性问题，然后提出了一种连续时间神经动力学方法。其次，获得了所考虑的优化问题的充分必要最优性条件。第三，证明了该方法的状态解的有界性、全局存在性和李雅普诺夫稳定性。此外，研究了对所考虑问题的最优解的收敛性。最后，