Complex Maneuverable Events Detection Based On REFNN

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.