Computer Simulations of FIFZN (Fuzzy Inverse-Free Zhang Neurodynamics) with Expected Precision Adaptively Satisfied Handling TVQP (Temporally-Variant Quadratic Programming)

Sicheng Zhu, Haifeng Hu, Min Yang, Yunong Zhang, Ning TAN

TL;DR: Verification of the efficacy and superiority of the FDZN model and numerical experiments about four specific TVQP examples.

Abstract: Some models have been used to solve temporally-variant (as well as time-dependent) quadratic programming (TVQP) in the last few years. But most of the models contain inverse matrix and time-invariant sampling gap, which will leads to computation errors and consumptions that we cannot control and do not want. Recently, a continuous inverse-free Zhang neurodynamics (CIFZN) model, also known as zeroing neural network model, is developed for solving TVQP. A fuzzy system is designed to adaptively adjust the sampling gap under the expected precision. Aided with the fuzzy system, an inverse free discrete model is further proposed and termed the fuzzy discrete Zhang Neurodynamics (FDZN) model. In this paper, verification of the efficacy and superiority of the FDZN model and numerical experiments about four specific TVQP examples are conducted. Most importantly, simulative is performed to illustrate the applicability of the FDZN model.

Submission Number: 78