Magnetic charge propagation in bulk frustrated materials has yielded a paradigm-shift science, allowing the symmetry between electricity and magnetism to be studied. Recent work is now suggesting magnetic dynamics upon spin-ice surface may have important implications determining ordering associated phase space. Here we detail 3D artificial spin-ice, nanostructured array of islands which capture...

An attempt has been made to simulate the temperature distribution in prostate cancer tissue during laser ablation using finite element approach. Parameter studies have been carried out. The results have been consolidated using tool of artificial intelligence-Artificial Neural Network. Feed forward back propagation network has been used for this purpose. It has been found that artificial neural ...

BACKGROUND The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling...

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee...

Back propagation neural network (BPNN) algorithm is a widely used technique in training artificial neural networks. It is also a very popular optimization procedure applied to find optimal weights in a training process. However, traditional back propagation optimized with Levenberg marquardt training algorithm has some drawbacks such as getting stuck in local minima, and network stagnancy. This...

In this paper back-propagation artificial neural network (BPANN )with Levenberg–Marquardt algorithm is employed to predict the deformation of the upsetting process. To prepare a training set for BPANN, some finite element simulations were carried out. The input data for the artificial neural network are a set of parameters generated randomly (aspect ratio d/h, material properties, temperature a...

This paper deals with artificial neural network (ANN) architecture, the multilayer Feed-forward (MLFF) network with back propagation learning. The training of an artificial neural network involves two passes. In the forward pass, the input signals propagate from the network input to the output. In the reverse pass the calculated error signals propagate backwards through the network where they a...