Abstract
Artificial neural networks can be used as intelligent controllers to control non-linear, dynamic systems through learning, which can easily accommodate the non-linearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. Taking benefit of the characteristics of a Generalized Neuron that requires much smaller training data and shorter training time, the pseudo-linear neural network (PNN) based model predictive approach used in the single and multi-machine power system studies is proposed in this paper. A simulation is carried out. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.
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© 2006 Springer-Verlag Berlin Heidelberg
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Fan, Y., Chen, Y., Li, S., Liu, D., Chai, Y. (2006). Adaptive Control for Synchronous Generator Based on Pseudolinear Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_195
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DOI: https://doi.org/10.1007/11760023_195
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
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