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Training is based on back propagation with adaptive training parameters.
A feed-forward neural network based on back propagation algorithm was developed to predict thermal performances of solar air collectors.
We present a new, efficient strategy for designing fully balanced transmission line RF circuits for solid state NMR probes based on back propagation of common impedance nodes (BPCIN).
Then, a feed-forward neural network based on back propagation algorithm was developed to model the thermal performance of the PFHEs.
A feed forward neural network based on back propagation was made up of 13 input neurons, 22 hidden neurons and one output neuron.
In this paper, we have proposed an optimal approach to build an effective anomaly NIDS based on Back Propagation Neural Network (BPNN) using Back Propagation Learning Algorithm, and employed a novel architecture for that network.
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A feed forward neural network based on back-propagation is a multilayered architecture made up of one or more hidden layers (2 layers–42 neurons) placed between the input (1 layer–5 neurons) and output (1 layer-1 neuron) layers.
According to the simulation results, the prediction model based on back-propagation networks predicted the debris flow most accurately.
In this paper, we propose a novel adaptive course control method based on Back-propagation Neural Network (BPNN) and Artificial Bee Colony (ABC) algorithm.
In this study compressibility factors of different compositions of natural gas are modeled by using an artificial neural network (ANN) based on back-propagation method.
This study presents a predictive mathematical model based on back-propagation neural network (BPNN) to relate femoral stem design parameters to the post-operative implant-bone micromotion, with no recourse to tedious nonlinear FE analysis.
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