Journal of Dalian Maritime University >
2015 , Vol. 41 >Issue 2: 41 - 45
DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2015.02.007
Neural network recognition model of ship lights based on clonal optimization
Received date: 2014-11-05
Revised date: 2014-12-08
Online published: 2015-06-07
Aiming at the features of ship lights recognition model under complex light environment such as strong nonlinearity, high dimension and complex environmental disturbances, a ship lights recognition model based on BP neural network was proposed. The relevant factors were selected as network inputs, and the identification code was set as network output to make up BP neural network model. Immune clonal selection optimization algorithm was adopted to decide the network layers and the number of units in each layer, by combining with sensitivity analysis method to search the direction and scale of nonlinear optimization to reduce the number of iterations the BP neural network and improve the search efficiency.Based on the spot photoes of ship lights, simulations of ship lights recognition were conducted by using the improved neural recognition model. Simulation results demonstrate the efficiency of the proposed ship lights recognition model.
Key words： ship lights; recognition model; clonal optimization; BP neural network
ZHU Jin-shan*1 , SUN Li-cheng2 , HU Jiang-qiang1 , HE Qing-hua1 . Neural network recognition model of ship lights based on clonal optimization[J]. Journal of Dalian Maritime University, 2015 , 41(2) : 41 -45 . DOI: 10.16411/j.cnki.issn1006-7736.2015.02.007
/
| 〈 |
|
〉 |