Abstract
In reality, the inputs of many complicated systems are continuous time-varying functions. It is difficult for traditional Elman neural networks (ENN) to simulate such complicated nonlinear systems directly because their inputs are all instantaneous constant values. To overcome this limitation, an Elman-style 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 health condition monitoring, and the application test results also indicate that the EPNN has a faster learning speed and a higher accuracy than the same scale ENN.
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© 2011 Springer-Verlag Berlin Heidelberg
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Ding, G., Lin, L. (2011). Elman-Style Process Neural Network with Application to Aircraft Engine Health Condition Monitoring. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21105-8_56
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DOI: https://doi.org/10.1007/978-3-642-21105-8_56
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