scholarly journals Spiking modular neural networks: A neural network modeling approach for hydrological processes

2006 ◽  
Vol 42 (5) ◽  
Author(s):  
Kamban Parasuraman ◽  
Amin Elshorbagy ◽  
Sean K. Carey
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Modeling ◽  
Hydrological Processes ◽  
Neural Network Modeling ◽  
Modular Neural Networks ◽  
Modeling Approach

scholarly journals SUPERIORITY OF ARTIFICIAL NEURAL NETWORKS OVER STATISTICAL METHODS IN PREDICTION OF THE OPTIMAL LENGTH OF ROCK BOLTS

2012 ◽  
Vol 18 (5) ◽  
pp. 655-661 ◽  
Author(s):  
Hadi Hasanzadehshooiili ◽  
Ali Lakirouhani ◽  
Jurgis Medzvieckas
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Statistical Models ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Rock Bolts ◽  
Optimal Length ◽  
The Neural Network ◽  
Rock Bolting ◽  
Geological Conditions

Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported that the optimal model was the network with the architecture of 6-18-3-1 as it demonstrated the minimum RMSE and MAE as well as the maximum R2. In comparison to statistical models (0.7182 for the value of R2 in the multiple linear regression model, 0.68 in the polynomial model and 0.7 in the dimensionless model), the results obtained by the neural network modeling – i.e. the coefficient of determination R2 of 0.9259, the value of mean absolute error MAE of 0.068, and the root mean squared error RMSE of 0.078 – not only proved their superiority but also introduced the neural network modelling as a highly capable prediction tool in forecasting the optimal length of rock bolts. Furthermore, sensitivity analysis was used to obtain parameters that have the greatest and the least impact on the optimal bolt length: the effect of the overburden thickness, tensile strength, cohesion and Poisson's ratio on the optimal bolt length was almost the same while the friction angle had the least influence.

Network Modeling of Fin-and-Tube Evaporator Performance Under Dry and Wet Conditions

2010 ◽  
Vol 132 (7) ◽  
Author(s):  
Ling-Xiao Zhao ◽  
Liang Yang ◽  
Chun-Lu Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Modeling ◽  
Cooling Capacity ◽  
Neural Network Modeling ◽  
Pressure Drops ◽  
Data Errors ◽  
Single Output ◽  
Dry And Wet Conditions ◽  
And Training

A new neural network modeling approach to the evaporator performance under dry and wet conditions has been developed. Not only the total cooling capacity but also the sensible heat ratio and pressure drops on both air and refrigerant sides are modeled. Since the evaporator performance under dry and wet conditions is, respectively, dominated by the dry-bulb temperature and the web-bulb temperature, two neural networks are used together for capturing the characteristics. Training of a multi-input multi-output neural network is separated into training of multi-input single-output neural networks for improving the modeling flexibility and training efficiency. Compared with a well-developed physics-based model, the standard deviations of trained neural networks under dry and wet conditions are less than 1% and 2%, respectively. Compared with the experimental data, errors fall into ±5%.

Mechanisms of developmental regression in autism and the broader phenotype: A neural network modeling approach.

2011 ◽  
Vol 118 (4) ◽  
pp. 637-654 ◽  
Author(s):  
Michael S. C. Thomas ◽  
Victoria C. P. Knowland ◽  
Annette Karmiloff-Smith
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Modeling Approach ◽  
Developmental Regression ◽  
Broader Phenotype

Neural network modeling of the implosion process in the tasks of optimization of oil production process

2021 ◽  
Author(s):  
A.R. Mukhutdinov ◽  
Z.R. Vakhidova ◽  
M.G. Efimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
Water Hammer ◽  
Design Parameters ◽  
Neural Network Modeling ◽  
Artificial Neural

An increase in the productivity of oil wells is possible with the use of a promising technology based on implosion and a device for its implementation. It is known that the effectiveness of the technology depends on the design parameters of the device. Currently, a promising way to study processes is computer modeling based on modern information technologies. Therefore, solving forecasting problems using modern software based on artificial neural networks (ANNs) is an urgent task of scientific and practical interest. In this regard, the aim of the work is to develop a neural network model and its application to identify the features of the influence of the diameter and length of the implosion chamber of the device on the pressure of a water hammer during implosion. In the software environment, the following have been created and tested: a method for developing a neural network model; a method of conducting a computational experiment with it. The possibility of neural network modeling of the implosion process has been studied. The results of predicting the output parameter, in this case the pressure of the water hammer, on a pre-trained network, with a relative error of 3.5%, using the knowledge base are demonstrated. The results of applying the methodology for solving forecasting problems using software based on artificial neural networks are presented. It was found that the diameter and length of the implosion chamber significantly affect the pressure of the water hammer. The practical significance of the work lies in the ability to determine the required values of the diameter and length of the implosion chamber of the device at a given level of water hammer pressure.

Sign in / Sign up

Export Citation Format

Share Document