TY - JOUR
T1 - Saturation-Allowed Zeroing Neural Networks activated by Various Functions for Time-varying Quadratic Programming
AU - Guo, Xiaoyu
AU - Liu, Mei
AU - Zhao, Qinglin
AU - Hu, Bin
AU - Lu, Huiyan
AU - Jin, Long
N1 - Publication date information for this publication is provided by the author(s) concerned.
PY - 2020/11/27
Y1 - 2020/11/27
N2 - Zeroing neural networks (ZNN) approach, has been presented to solve a lot of time-varying problems activated by monotonically increasing functions. However, the existing ZNN models for timevarying quadratic programming based on ZNN approach may be different from each other in structures, but share two common restrictions, i.e., the function must be convex and unbounded. In order to relax the above restrictions in solving time-varying quadratic programming (TVQP) problems, this paper proposes a saturation-allowed zeroing neural networks (SAZNN) model based on the ZNN approach. Comparing with existing models, the activation function (AF) of SAZNN model tolerates more kinds of functions, e.g., saturation function, non-convex function and unbounded function. Finally, this paper provides simulation results synthesized by the proposed SAZNN model activated by various AFs and verifies the superiority of the proposed SAZNN model in terms of convergence, efficiency and stability.
AB - Zeroing neural networks (ZNN) approach, has been presented to solve a lot of time-varying problems activated by monotonically increasing functions. However, the existing ZNN models for timevarying quadratic programming based on ZNN approach may be different from each other in structures, but share two common restrictions, i.e., the function must be convex and unbounded. In order to relax the above restrictions in solving time-varying quadratic programming (TVQP) problems, this paper proposes a saturation-allowed zeroing neural networks (SAZNN) model based on the ZNN approach. Comparing with existing models, the activation function (AF) of SAZNN model tolerates more kinds of functions, e.g., saturation function, non-convex function and unbounded function. Finally, this paper provides simulation results synthesized by the proposed SAZNN model activated by various AFs and verifies the superiority of the proposed SAZNN model in terms of convergence, efficiency and stability.
KW - Saturation-allowed zeroing neural networks (SAZNN)
KW - Time-varying quadratic programming (TVQP)
KW - Convergence
UR - http://www.scopus.com/inward/record.url?scp=85104232283&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85104232283&origin=recordpage
U2 - 10.2298/FIL2015149G
DO - 10.2298/FIL2015149G
M3 - RGC 21 - Publication in refereed journal
SN - 0354-5180
VL - 34
SP - 5149
EP - 5157
JO - Filomat
JF - Filomat
IS - 15
ER -