[1]FU Weiyang,LIU Yian,XUE Song.Target threat assessment using improved Krill Herd optimization and extreme learning machine[J].CAAI Transactions on Intelligent Systems,2018,13(5):693-699.[doi:10.11992/tis.201704007]
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Target threat assessment using improved Krill Herd optimization and extreme learning machine
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 5
Page number:
693-699
Column:
学术论文—机器学习
Public date:
2018-09-05
- Title:
- Target threat assessment using improved Krill Herd optimization and extreme learning machine
- Keywords:
- target threat assessment; Krill Herd algorithm; extreme learning machine; opposition-based learning; neural networks; weights; thresholds; threat estimation model
- CLC:
- TP391.9
- DOI:
- 10.11992/tis.201704007
- Abstract:
- To improve the accuracy of target threat estimation, the opposition-based learning Krill Herd optimization (OKH) and extreme learning machine (OKH-ELM) model is established, and the algorithm based on the model is presented. The OKH-ELM adopts opposition-based learning (OBL) to optimize KH, and then the improved KH and extreme learning machine are employed to simultaneously optimize the initial input weights and offsets of the hidden layer in ELM. A target threat database is adopted to test the performance of OKH-ELM in target threat prediction. The experimental result shows that OKH Algorithm can better optimize the weights and thresholds of the hidden layer in ELM and improve the prediction precision and generalization ability of the target threat assessment model; therefore, it can accurately and effectively estimate target threat.
- References:
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Last Update:
2018-10-25