Abstract Machine learning algorithms can study existing data to perform specific tasks. One of the well-known machine is backpropagation algorithm, but this algorithm often provides poor convergence speed in training process and a long time. The purpose optimize standard using Beale-Powell conjugate gradient so that time needed achieve not too long, which later be used as reference information ...

A Bayesian regularization-backpropagation neural network (BR-BPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. variation maximum normal and tangential pull-off forces resultant force angle at detachment with peeling angle. K-fold cross validation used improve effectiveness model. The input data taken from finite element (FE) results. trained 75% FE dataset. rema...

We propose a novel three-layered neural network-based architecture for predicting the Sixteen Personality Factors from facial features analyzed using Facial Action Coding System. The proposed architecture is built on three layers: a base layer where the facial features are extracted from each video frame using a multi-state face model and the intensity levels of 27 Action Units (AUs) are comput...

Recurrent Backpropagation and Equilibrium Propagation are algorithms for fixed point recurrent neural networks which differ in their second phase. In the first phase, both algorithms converge to a fixed point which corresponds to the configuration where the prediction is made. In the second phase, Recurrent Backpropagation computes error derivatives whereas Equilibrium Propagation relaxes to an...