Problems about target classification in situation assessment are analyzed. This paper presents a syntheticmethod for fulfilling target classification by using the nearest-neighbor method and field knowledge. The graphicalstructure formed by target classification is shown by the adjacency list. Based on the structure, breadth-first searchalgorithm is used for the implementation of dynamic maintenance. The output of target classification is helpful to de-termine the interaction among situation elements, thus interprets actions related to problem field.
It is important to select input variables when the neural network forecasting model is proposed. In this pa-per, by using the autocorrelation function on input variables sets selection for neural network forecasting model, asystemic and scientific method for input variables sets selection is put forward. FFT is adopted to accomplish thespeediness calculation, which enhances the maneuverability of this approach. A forecasting example is given, whoseresult indicates that the method is effective.
