Abstract: Flight fault inspection which will be one of the key problems in pilot-aided equipment in the next generation of avionic system is first considered to be a nonlinear mapping,or classifi-cation problem,which provides a possibility to solve it with artificial neural networks(ANN).To overcome the problems of learning speed and classification accuracy in data classification with ANNs,the Double Parallel Feedforward Neural Network(DPFNN) model with encoded input is developed and training algorithms for MLP-like networks are extended to training the DPFNNs.TheDPFNN considered to be a parallel connection of an MLFNN with a SLFNN is expected to have better learning speed and classification accuracy,since the SLFNN can give a li near solution very fast and then the M LFNN uses its hidden nodes to adjust that solution to improve the DPFNN’ s performance. A few flight fauIt patterns as study cases are simulated on computer and used to test the DPFNN model.The experiment results have shown their good agreement with theoretical consideration。

Key words: flight hazards-faults-inspection, neural nets, feedforward control, simulations