¹Engineering College, Air Force Engineering University, Xi’an, 710038, China Xuzhou Air Force College, Xuzhou, 221000, China
²College of Science , Air Force Engineering University, Xi’an, P.R.China, 710051
³Xuzhou Air Force College, Xuzhou, 221000, China

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Abstract—In this paper, a method based on REFNN (Rough- Evolution Fuzzy Neural Network) is proposed to deal with such problems as imprecision and poor real-time performance in complex maneuverable events detection. Firstly, the optimal discrete values of continuous attributes are obtained through GA (Genetic Algorithm); secondly, the minimal rule sets from data samples are acquired by using the Rough Set Theory; then, these rules are used to construct the initial scalar values of neural cells in each layer and their relative parameters in the fuzzy neural network; lastly, parameters of the network are acquired by using BP(back propagation) algorithm. The simulation shows the effectiveness of the new method of complex maneuverable events detection based on REFNN; simultaneously REFNN has structure advantages.

Index Terms—situation assessment, complex maneuverable events, events detection, REFNN.

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Cite:Wei Chen, Ji-zheng Wu, Qing Li, and Peng Hui Rong, "Complex Maneuverable Events Detection Based On REFNN," Journal of Software vol. 7, no.1, pp. 81-87, 2012.