In this paper we present a novel algorithm to learn a score distribution over the nodes of a labeled graph (directed or undirected). Markov Chain theory is used to define the model of a random walker that converges to a score distribution which depends both on the graph connectivity and on the node labels. A supervised learning task is defined on the given graph by assigning a target score for ...

Signal processing algorithms such as neural network learning, convolution, cross-correlation, IIR ltering, etc., can be computationally time-consuming and are often used in time-critical application. This makes it desirable to achieve high e ciency on these routines. Such algorithms are often coded in assembly language to achieve optimal speed, but it is then di cult to make a full exploration ...

Artificial neural networks are most commonly trained with the back-propagation algorithm, where the gradient for learning is provided by back-propagating the error, layer by layer, from the output layer to the hidden layers. A recently discovered method called feedback-alignment shows that the weights used for propagating the error backward don’t have to be symmetric with the weights used for p...

Artificial neural networks are most commonly trained with the back-propagation algorithm, where the gradient for learning is provided by back-propagating the error, layer by layer, from the output layer to the hidden layers. A recently discovered method called feedback-alignment shows that the weights used for propagating the error backward don’t have to be symmetric with the weights used for p...

The back propagation (BP) neural network algorithm is a multi-layer feedforward network trained according to error back propagation algorithm and is one of the most widely applied neural network models. BP network can be used to learn and store a great deal of mapping relations of input-output model, and no need to disclose in advance the mathematical equation that describes these mapping relat...

in this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (lims). dynamical equations of lim have been considered accurate. in other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. then equations of the reference model and adaptive mode...

To counter the drawbacks that Waibel 's time-delay neural networks (TDW) take up long training time in phoneme recognition, the paper puts forward several improved fast learning methods of 1PW. Merging unsupervised Oja's rule and the similar error back propagation algorithm for initial training of 1PhW weights can effectively increase convergence speed, at the same time error firnction almost m...

This study presents a new procedure based on Artificial Neural Network (ANN) for identification of double cracks in Functionally Graded Beams (FGBs). A cantilever beam is modeled using Finite Element Method (FEM) for analyzing a double-cracked FGB and evaluation of its first four natural frequencies for different cracks depths and locations. The obtained FEM results are verified against availab...

In this paper, the study of restoration of Supersymmetry, broken at tree level, has been undertaken. In the present model we have lucratively applied the method of back propagation based on gradient descent along the error surface to restore the supersymmetry against arbitrary tiny quantum corrections in the form of small thermal agitations. Back propagation method brings the potential stabilit...