Handling uncertainty in portfolio optimization: A neutrosophic logic adaptive neural network solver for quadratic programming.

Abstract

Numerous domains, including robotics and artificial intelligence, make extensive use of time-varying quadratic programming (TVQP). Because of the TVQP's importance, a novel adaptive neutrosophic logic/fuzzy neural network TVQP solver, called NZNN-TVQP, is introduced in this work. The proposed TVQP solver uses a recently developed neutrosophic logic/fuzzy adaptive zeroing neural network (NZNN) technique as well as a neutrosophic logic/fuzzy adaptive penalty function. It is important to mention that the NZNN is an advancement on the conventional zeroing neural network (ZNN) technique, which has shown great promise in solving time-varying tasks. To address the TVQP task, the performance of four variations of the NZNN-TVQP solver are examined. All variations of the solver perform remarkably well, as demonstrated by two simulation tests and two real-world applications to portfolio selection problem.