Supervisor:
Southwest Ordnance Industry Bureau
Organizer:
Chongqing Ordnance Industry Society
Chongqing University of Technology
Chongqing University of Technology
Research on high performance evaluation method of seeker based on small sample RBF neural network
| DOI: | 10.11809/bqzbgcxb2023.09.021 |
| Keywords: | small sample; RBF neural network; TOPSIS method; Bootstrap method; effectiveness evaluation |
| Abstract: | Aiming at the problem of lacking test data due to high test cost of missile weapon equipment, a small sample RBF neural network model is proposed. The original data is processed by TOPSIS method, the deep information of data is fully mined, the processed data is expanded by Bootstrap method, the evaluation model is established by radial basis function (RBF) neural network, and the small sample RBF neural network model is applied to the high performance evaluation of seeker. The simulation results show that: the decision coefficient and error coefficient of small sample RBF neural network model are significantly improved compared with other models. The model not only avoids the subjectivity of expert method, analytic hierarchy process, and fuzzy comprehensive evaluation method, but also solves the problem of small sample size. |
| Issue: | Vol. 44 No. 9 (2023) |
| Published: | 2023-09-28 |
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