Design and analysis of recurrent neural network models with non‐linear activation functions for solving time‐varying quadratic programming problems
Xiaoyan Zhang, Liangming Chen, Shuai Li, Predrag S. Stanimirović, Jiliang Zhang, Long Jin
Abstract
Abstract A special recurrent neural network (RNN), that is the zeroing neural network (ZNN), is adopted to find solutions to time‐varying quadratic programming (TVQP) problems with equality and inequality constraints. However, there are some weaknesses in activation functions of traditional ZNN models, including convex restriction and redundant formulation. With the aid of different activation functions, modified ZNN models are obtained to overcome the drawbacks for solving TVQP problems. Theoretical and experimental research indicate that the proposed models are better and more effective at solving such TVQP problems.
Topics & Concepts
Quadratic programmingRecurrent neural networkArtificial neural networkMathematical optimizationQuadratic equationComputer scienceLinear programmingConvex optimizationMathematicsRegular polygonArtificial intelligenceGeometryAdvanced Control Systems OptimizationMetaheuristic Optimization Algorithms ResearchFuzzy Logic and Control Systems