Helicopter engine performance prediction based on cascade-forward process neural network

In view of the difficulty of predicting engine performance effectively in traditional methods, a prediction method based on CFPNN (Cascade-Forward Process Neural Network) is proposed. By introducing a set of appropriate orthogonal basis functions into the input space, the input functions and weight functions are expanded. The time aggregation operation of the process neurons is simplified by this way. The RBP (Resilient Back-Propagation) learning algorithm based on orthogonal basis function expansion is proposed. The CFPNN based on RBP learning algorithm is compared with FFPNN (Feed-Forward Process Neural Network) based on RBP learning algorithm and CFPNN based on ABP (Adaptive Back-Propagation) learning algorithm respectively. The results show that the CFPNN based on RBP learning algorithm possesses good convergence and high accuracy. It provides an effective way for helicopter engine performance prediction.