A New Neurodynamics-Based Model for Fuzzy Convex Optimization Problems With Fuzzy Coefficients and General Constraints

Fuzzy convex optimization problems with fuzzy coefficients (FCOPFCs) arise in many applications. Although many neurodynamics-based models have been proposed for solving FCOPFCs, most of them are designed for FCOPFCs with equality or inequality constraints only. However, in many applications, the FCOPFCs often come with both equality and inequality constraints (general constraints, for short), so most of the neurodynamics-based models no longer work in these situations. Therefore, this article aims to construct a new model for FCOPFCs with general constraints to extend the applications of neurodynamics-based models. First of all, based on fuzzy set theory, the original FCOPFCs with general constraints is transformed into a series of interval programming tasks and further transformed into crisp optimization problems with weights. Then, a novel continuous-time neurodynamics-based model with a single-layer structure is established to solve the crisp optimization problem with weights. Further, we discuss the global existence and prove the stability of state solutions. The theoretical results show that the state solutions reach the feasible region within finite time and converge to the optimal solution with the smallest 2-norm. Simulation results completed for three kinds of FCOPFCs show the validity of the approach, and the results in real-world applications demonstrate the excellent performance of the proposed model.