Helicopter engine performance prediction based on cascade-forward process neural network
- Resource Type
- Conference
- Authors
- Yao-ming, Zhou; Zhi-jun, Meng; Xu-zhi, Chen; Zhe, Wu
- Source
- 2012 IEEE Conference on Prognostics and Health Management Prognostics and Health Management (PHM), 2012 IEEE Conference on. :1-5 Jun, 2012
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engines
Biological neural networks
Prediction algorithms
Engine cylinders
Neurons
Training
Cascade-Forward Process Neural Network
Resilient Back-Propagation learning algorithm
prediction
orthogonal basis function
- Language
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.