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