Mv, Jiju (2008) Time series prediction and channel equalizer using artificial neural networks with VLSI implementation. MTech thesis.
The architecture and training procedure of a novel recurrent neural network (RNN), referred to as the multifeedbacklayer neural network (MFLNN), is described in this paper. The main difference of the proposed network compared to the available RNNs is that the temporal relations are provided by means of neurons arranged in three feedback layers, not by simple feedback elements, in order to enrich the representation capabilities of the recurrent networks. The feedback layers provide local and global recurrences via nonlinear processing elements. In these feedback layers, weighted sums of the delayed outputs of the hidden and of the output layers are passed through certain activation functions and applied to the feedforward neurons via adjustable weights. Both online and offline training procedures based on the backpropagation through time (BPTT) algorithm are developed. The adjoint model of the MFLNN is built to compute the derivatives with respect to the MFLNN weights which are then used in the training procedures. The Levenberg–Marquardt (LM) method with a trust region approach is used to update the MFLNN weights. The performance of the MFLNN is demonstrated by applying to several illustrative temporal problems including chaotic time series prediction and nonlinear dynamic system identification, and it performed better than several networks available in the literature.
|Item Type:||Thesis (MTech)|
|Uncontrolled Keywords:||VLSI, artificial neural networks|
|Subjects:||Engineering and Technology > Electronics and Communication Engineering > Artificial Neural Networks|
|Divisions:||Engineering and Technology > Department of Electronics and Communication Engineering|
|Deposited By:||Hemanta Biswal|
|Deposited On:||10 Jul 2012 14:24|
|Last Modified:||10 Jul 2012 14:24|
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