From Zhang Neural Network to Newton Iteration for Matrix Inversion

Different from gradient-based neural networks, a special kind of recurrent neural network (RNN) has recently been proposed by Zhang for online matrix inversion. Such an RNN is designed based on a matrix-valued error function instead of a scalar-valued error function. In addition, it was depicted in...

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Vydané v:	IEEE transactions on circuits and systems. I, Regular papers Ročník 56; číslo 7; s. 1405 - 1415
Hlavní autori:	Yunong Zhang, Yunong Zhang, Weimu Ma, Weimu Ma, Binghuang Cai, Binghuang Cai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2009 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Activation function Control systems Convergence Difference equations Dynamics Error functions initial state Inversions Iterative methods Mathematical models matrix inversion Neural networks Newton iteration On-line systems Online recurrent neural network (RNN) Recurrent neural networks Robot control Signal processing algorithms Stability Statistics step size Studies
ISSN:	1549-8328, 1558-0806
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