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A novel one-layer neural network for solving quadratic programming problems
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AI
DOI：10.1016/j.neunet.2025.107344.png)
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Abstract
En 中文
This paper proposes a novel one-layer neural network to solve quadratic programming problems in real time by using a control parameter and transforming the optimality conditions into a system of projection equations. The proposed network includes two existing dual networks as its special cases, and an existing model can be derived from it. In particular, another new model for linear and quadratic programming problems can be obtained from the proposed network. Meanwhile, a new Lyapunov function is constructed to ensure that the proposed network is Lyapunov stable and can converge to an optimal solution of the concerned problem under mild conditions. In contrast with the existing models for quadratic programming, the proposed network requires the least neurons while maintaining weaker stability conditions. The effectiveness and characteristics of the proposed model are demonstrated by the limited simulation results.
Keywords:
Convergence
Lyapunov function
Neural network
Quadratic programming
Stability
Journal
IF:
6.3
Papers:
7.8K
Citations:
3.0W
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