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Abstract: A
quadratic diagonal recurrent neural network(QD-RNN) is presented. The
hidden layer of QDRNN is comprised of quadratic self-recurrent neuron,
so the QDRNN is dynamic mapping. Two QDRNNs are utilized in a ship-gun
control system, one is an identifier called quadratic diagonal recurrent
neuroidentifier(QDRNI) and the other as a controller called quadratic
diagonal recurrent neurocontroller(QDRNC). The hydraulic servo system of
ship-gun is identified by the QDRNI, which then provides the sensitivity
information of the hydraulic servo system to the QDRNC. A generalized
dynamic backprop-agation algorithm is developed and used to train both
QDRNC and QDRNI. Due to the quadratic recurrence, the QDRNN can capture
the dynamic behavior of the servo system. The test results for the
hydraulic servo system indicate that the control precision of QDRNN is
high and the adaptability is strong.
Key words: Recurrent neural network Test study
Warship-guns
CLC No: TG156
Received
20040705, received in revised form 20041115
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