Sentence examples for back propagation based on from inspiring English sources

This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift.

In this paper, the performance of the ILRBF-BP algorithm is compared with other well-known learning algorithms, such as back propagation based on stochastic gradient descent (SGBP) [29], the RBF algorithm based on k-means clustering (KM-RBF) [12], GAP-RBF, SVM, and an ELM, on artificial data sets.

Elman network (EN) is optimally designed and trained using static back propagation algorithm based on the optimization of performance measures such as mean square error, correlation coefficient and mean absolute percentage error on test prediction dataset.

Back propagation is based on minimization of a suitable error or cost function.

Common back propagation is based on the diffraction integral in the Fresnel approximation to calculate the complex wavefield Q (x ', y ' ) in the image plane (x ', y ' ) : Q (x ', y ' ) = 1 i λ ∬ h r e i k ρ ρ cos Ω d ξ d η where h is the hologram in the plane.

We will use the representation learning techniques including word embeddings and compositional vector models, and apply a back-propagation algorithm based on gradient descent to learn the model.

BP (back propagation) neural network based on Levenberg-Marquardt algorithm was used for predicting the rolling force.

The ANN predictive models of surface roughness was developed using a multilayer feed forward neural network and trained with the help of an error back propagation learning algorithm based on the generalized delta rule.

After introducing a Back Propagation neural network based on Particle Swam Optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection.

Peng and Zhang [77] proposed a back propagation (BP) algorithm based on modified error function to overcome a major problem of long training time of the BP algorithm and chance of falling into local minima.

The back-propagation training algorithm based on Levenberg-Marquardt back propagation.