System Effectiveness Evaluation Method Based on GA-BP under the Condition of Small Sample

In the evaluation of system effectiveness, the factors affecting system effectiveness are becoming more and more complex and the relationship between them is obviously nonlinear. The Genetic Algorithm-Error Back Propagation(GA-BP) neural network evaluation method has a wide application space due to its good nonlinear fitting ability and good convergence. However, the sample data for system effectiveness evaluation is usually small sample data, which increases the randomness of the evaluation results of the GA-BP neural network evaluation method with sample dependence. The accuracy of the assessment is reduced. In view of this, based on the bootstrap method and the non-replacement sampling method, two GA-BP-based system effectiveness evaluation methods under the condition of small sample are proposed, which are verified by using the sample data. Compared with the GA-BP neural network evaluation method, the two evaluation methods have significantly improved evaluation accuracy, and the evaluation method optimized based on the non-replacement sampling method has better performance.