An improved membrane algorithm for solving time-consuming water quality retrieval
Retrieving the parameters in water quality with multispectral data using neural network is increasingly popular, however, the training process with large amount samples and calculation with large-volume data are a time-consuming work. Many emergency pollution events need quick responses for practical use. In this paper, an improved membrane computing strategy is presented. This strategy is a hybrid one combining the framework and evolution rules of P systems with active membranes and neural networks, and it involves a dynamic structure including membrane fusion and division, which helpful to enhance the information communication and beneficial to reduce the computation. Then, a parallel implementation with the training result is discussed. Experiments with Landsat datasets to obtain suspended sediment are carried out to demonstrate the practical capabilities of this introduced strategy.
water quality multispectral data suspended sediment membrane computing neural networks
Liang Zhong Wenfei Luo
Guangdong Technical College of Water Resources and Electric Engineering, Guangzhou, China,510631 School of Geography Science, South China Normal University, Guangzhou, China,510631
国际会议
桂林
英文
1-7
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）