Numerical Simulations of Filtered Reciprocal-Kind Zhang Neural Network for Solving Time-Varying QP Problems
In our previous work [1], we introduced the filtered reciprocal-kind Zhang neural network (FRKZNN) model for handling time-varying quadratic programming (QP) problems. In this paper, we conduct two sets of numerical simulations to evaluate the model’s generalization, analyze the influence of its parameters, and perform an ablation study. The results from these simulations demonstrate the effectiveness and superiority of the proposed FRKZNN model.
Authors
- Yunong Zhang (ORCID: https://orcid.org/0000-0002-2228-0395)
- Zhiguo Tan (ORCID: https://orcid.org/0000-0002-9450-9777)
- Pengfei Guo (ORCID: https://orcid.org/0000-0002-7662-9070)
Institutions
- Sun Yat-sen University (CN)
- Guangzhou Panyu Polytechnic (CN)
- Zhongkai University of Agriculture and Engineering (CN)
Publication Details
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1109/irac63143.2024.10871789
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China