Orthogonal Projected Gradient Differential Neural Solution to Linear and Quadratic Constrained Optimization: Theory and Applications
Abstract
In this research, an orthogonal projected gradient differential neural solution (OPGDNS) is introduced, specifically tailored for addressing linear and quadratic constrained optimization (LQCO) problems. The orthogonal projection theorem and gradient information are strategically leveraged, enabling the proposed solution to exhibit superior efficacy over existing methods, particularly those derived from a differential neural solution (DNS) standpoint, in handling linear and quadratic constraints. Theorems and proofs concerning the stability and convergence of the proposed OPGDNS model for LQCO problems are established. Finally, a numerical example is given, and experiments on the robust adaptive beamforming and the redundant robot are implemented, substantiating the applicability and advantage of the proposed OPGDNS model.
Fields of Study & Subjects
Neural Networks and Applications
Neural Networks Stability and Synchronization
Model Reduction and Neural Networks
Publication details
- Journal / source
- IEEE Transactions on Systems Man and Cybernetics Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 56
- Issue
- 7
- Pages
- 3910–3922
- Publication date
- 26 February 2026