@Article{cmc.2020.05625,
AUTHOR = {Haoxiang Sun, Changxing Chen, Yunfei Ling, Mu Yang},
TITLE = {Cooperative Perception Optimization Based on Self-Checking Machine Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {2},
PAGES = {747--761},
URL = {http://www.techscience.com/cmc/v62n2/38274},
ISSN = {1546-2226},
ABSTRACT = {In the process of spectrum perception, in order to realize accurate perception of 
the channel state, the method of multi-node cooperative perception can usually be used. 
However, the first problem to be considered is how to complete information fusion and 
obtain more accurate and reliable judgment results based on multi-node perception results. 
The ideas put forward in this paper are as follows: firstly, the perceived results of each node 
are obtained on the premise of limiting detection probability and false alarm probability. 
Then, on the one hand, the weighted fusion criterion of decision-making weight optimization 
of each node is realized based on a genetic algorithm, and the useless nodes also can be 
screened out to reduce energy loss; on the other hand, through the linear fitting ability of 
RBF neural network, the self-inspection of the perceptive nodes can be realized to ensure the 
normal operation of the perceptive work of each node. What's more, the real-time training 
data can be obtained by spectral segmentation technology to ensure the real-time accuracy of 
the optimization results. Finally, the simulation results show that this method can effectively 
improve the accuracy and stability of channel perception results, optimize the structure of 
the cooperative network and reduce energy consumption.},
DOI = {10.32604/cmc.2020.05625}
}