Abstract
A design method of RBF neural network construction based on rough sets and orthogonal least squares (OLS) is proposed in this paper. First, the rough sets knowledge expression system is built taking use of the large number of system sample data. The impact of input variables on output variables is analyzed through computing an accuracy measure of input space knowledge on output space knowledge with reducing the input space of the knowledge expression system, System order and the input layer nodes of neural network can be decided by this way. Secondly, the hide layer nodes and the weights of output layer of the neural network can be obtained using OLS algorithm. Finally, the research of simulations on a nonlinear system are carried out using this method in this paper, the research results show the method is effective and feasible.
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