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Scaling CMOS integrated circuit technology leads to decrease the chip price and increase processing performance in complex applications with re-configurability. Thus, VLSI architecture is a promising candidate in implementing neural network models nowadays. Backpropagation algorithm is used for training multilayer perceptron with high degree of parallel processing. Parallel computing implementation...
A neural network (NN) pruning method optimized with particle swarm optimization (PSO) algorithm is proposed in this paper. Correlation merging algorithm is an important pruning method in NN structure design. Unlike general training method with back-propagation (BP), this paper uses PSO algorithm in the pruning process. The PSO is used to optimize the initial parameters of the NN, including the weights...
Artificial neural networks (ANNs) have been applied to a variety of classification and learning tasks. The use of evolutionary algorithms (EA) as one of the fastest, robust and efficient global search techniques has allowed different properties of artificial neural networks to be evolved. This paper proposes the possibility of using differential evolution for determining an ANN architecture (DNNA)...
In order to experiment the performance of some popular ANN algorithms to OMIS (Operational Modular Imaging Spectrometer) hyperspectral image, three widely used ANNs, including Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Fuzzy ARTMAP network and their improvements, are employed and compared. It is concluded that ANN classifiers perform much better than traditional...
This paper describes and discusses a novel approach that uses artificial neural networks (ANN) to extend the measurement range of fiber Bragg gratings (FBGs) interrogators based on fixed narrowband filter demodulation. Interrogators with fixed spectral filters use only one edge of the filter to demodulate the signal. The proposed method uses narrowband FBG's filters, where both edges of the entire...
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