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| In reality, the inputs of many complicated systems are continuous time-varying functions or processes. It is difficult for traditional Elman neural networks (ENN) to simulate such complicated nonlinear systems directly because their inputs are all instantaneous constant values. In order to overcome this limitation, an Elman process neural network (EPNN) is proposed in this paper. From the point view of architecture, the EPNN is similar to the ENN. The major characteristics which distinguish the EPNN from the ENN lie in the fact that the inputs and the connection weights of the EPNN are time-varying functions. A corresponding learning algorithm based on the expansion of the orthogonal basis functions is developed. The effectiveness of the EPNN and its learning algorithm is proved by the lubricating oil iron concentration prediction in the aircraft engine condition monitoring, and the application test results indicate that the EPNN has a faster learning speed and a higher accuracy than the same scale ENN. |
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| Keywords:Elman process neural network, learning algorithm, aircraft engine condition monitoring. |
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