Whether it is training artificial

neural networks on the Intel Nervana cloud, optimizing for emerging workloads such as artificial intelligence, virtual and augmented reality, and automated driving with Intel Xeon Scalable processors, or taking AI to the edge with Movidius vision processing unit (VPU) technology, Intel offers a comprehensive AI portfolio of tools, training and deployment options for the next generation of AI-powered products and services.

In this research work, the adopted methodology is based upon the combination of seismic attributes, acoustic impedance, processed logs and artificial

neural network training (Basu and Verma, 2013).

Mattheij is currently waiting for the

neural network to learn to classify all the bricks by itself before starting his business of selling Lego bricks to other Lego enthusiasts.

In this paper, we integrate quantum particle swarm optimization and RBF

neural network model together to construct a hybrid model, and the output vector of RBF

neural networks acts as logistics requirements forecasting results.

Hence the current study uses Multilayer Perceptron of

neural network to explore the relationship between job satisfiers such as pay, promotion, supervisor, benefits, rewards, operating procedure, coworkers, work and communication and work commitmentand also to identify the contribution of each individual job satisfiers towards work commitment which is a novel approach and vital for the current business scenario.

The most common activation functions of RBF

neural network are Gaussian, Multiquadric, Inverse Quadratic, Inverse Multiquadric and Polyharmonic spline [8].

Thus, need of

neural network model operation of potential of metal on a newformed surface is traced, for tutoring of a

neural network results of the conducted laboratory researches will be used [8].

Most models of artificial

neural network are nonlinear dynamic system.

The learning objective for the

neural network is to "estimate" the time necessary to reach the target temperature which is programmed into the room control system.

Neural network models consist of simple computational units organized into a sequence of layers and interlinked by a system of connections.

Neural network modeling applied to reduce computational complexity and to approximate the controller.

As a highly adaptive regression technique that can be easily modified and is suitable for predictive analysis, computerized

neural network analysis has been previously used to transform nomograms and to evaluate dosing of HMG CoA-reductase inhibitors.

This vector of degree of membership is now being trained into the

neural network for generating the final classification of rules using the Backpropagation algorithm of

neural network.

These data were analyzed with the help of Statistica

Neural Network software, which, besides its use for training and choosing the more capable

neural networks to model the chosen variable, allows the selection of the group of variables of larger influence in the studied process.

This paper surveys research on Emulative

Neural Network (ENN) models as economic forecasters.