The visual presentation of evaluation objectivity works of different kinds of

neural networks for chrome steels: 1--the potential of 12Cr18N10T with the oxide film, mV; 2--the potential of 12Cr18N10T without the oxide film, mV; 3--the potential of 20Cr13 with the oxide film, mV; 4--the potential of 20Cr13 without the oxide film, mV; 5--the potential of 10CrSND with the oxide film, mV; 6--the potential of 10CrSND without the oxide film, mV.

Su Peng from Harbin Institute of Technology created a recurrent

neural network to solve the pseudo-convex optimization problem with linear equation constraint condition in the perspective of variational inequality in Research on Several Kinds of Nonlinear Optimization Problems Based on

Neural Network [2].

The work and research of

neural networks for the time being is limited to the specific scenario of room temperature control, with future perspectives to more small and easy-to-use applications within smart home projects.

In parametric cost modeling

neural networks could quantify the impact of parameters on the cost by capturing the relations existing in the historical project data.

In general, these approaches can be divided into two groups: in the first one, special structures of neural models are used to make the optimization problem simpler (convex), while in the second, explicit approximate algorithms (without on-line optimization) combined with

neural networks are used.

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.

Human performs the entire decision-processing task with biological

neural networks [1], which consists of the interconnection of neurons in an immensely intricate fashion.

Tune: Layer-by-layer performance metrics for both industry-standard and custom-designed

neural networks enable effective tuning for optimal real-world performance at ultra-low power.

After validation the porosity cube is generated, which showed a very good correlation results between porosity predicted through probabilistic

neural networks and porosity estimated from well log data.

He then decided to create an automated sorting machine powered by a

neural network and wrote an article titled 'Neural Nets vs.

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.

India, June 15 -- Apple has announced that it will be giving developers access to its

neural networks.

The majority of previous studies used logistic regression analysis and several statistical tools for analyzing the data and some study, compared

neural networks with conventional statistical tools to evaluate the job satisfaction attributes, but this is a study which uses

neural network to find out the normalized importance of individual job satisfiers of employees towards work commitment and more specifically this study uses Multilayer Perceptron

neural network model which is more advantageous than some of the other statistical tools like logistic regression analysis (Huang 2012).

As these models artificial

neural networks are widely used because of their proven quality of generalization and approximation of almost any smooth functions [4].

APPLICATION:

Neural networks may be useful to help control moisture content and increase dryer efficiency.