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Artificial Neural Network Modeling (artificial + neural_network_modeling)
Selected AbstractsARTIFICIAL NEURAL NETWORK MODELING FOR REFORESTATION DESIGN THROUGH THE DOMINANT TREES BOLE-VOLUME ESTIMATIONNATURAL RESOURCE MODELING, Issue 4 2009MARIA J. DIAMANTOPOULOU Abstract In the management of restoration reforestations or recreational reforestations of trees, the density of the planted trees and the site conditions can influence the growth and bole volume of the dominant tree. The ability to influence growth of these trees in a reforestation contributes greatly to the formation of large dimension trees and thereby to the production of commercially valuable wood. The potential of two artificial neural network (ANN) architectures in modeling the dominant,Pinus brutia,tree bole volume in reforestation configuration at 12 years of age was investigated: (1) the multilayer perceptron architecture using a back-propagation algorithm and (2) the cascade-correlation architecture, utilizing (a) either the nonlinear Kalman's filter theory or (b) the adaptive gradient descent learning rule. The incentive for developing bole-volume equations using ANN techniques was to demonstrate an alternative new methodology in the field of reforestation design, which would enable estimation and optimization of the bole volume of dominant trees in reforestations using easily measurable site and competition factors. The usage of the ANNs for the estimation of dominant tree bole volume through site and competition factors can be a very useful tool in forest management practice. [source] Artificial neural network modeling of RF MEMS resonatorsINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 4 2004Yongjae Lee Abstract In this article, a novel and efficient approach for modeling radio-frequency microelectromechanical system (RF MEMS) resonators by using artificial neural network (ANN) modeling is presented. In the proposed methodology, the relationship between physical-input parameters and corresponding electrical-output parameters is obtained by combined circuit/full-wave/ANN modeling. More specifically, in order to predict the electrical responses from a resonator, an analytical representation of the electrical equivalent-network model (EENM) is developed from the well-known electromechanical analogs. Then, the reduced-order, nonlinear, dynamic macromodels from 3D finite-element method (FEM) simulations are generated to provide training, validating, and testing datasets for the ANN model. The developed ANN model provides an accurate prediction of an electrical response for various sets of driving parameters and it is suitable for integration with an RF/microwave circuit simulator. Although the proposed approach is demonstrated on a clamped-clamped (C-C) beam resonator, it can be readily adapted for the analysis of other micromechanical resonators. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14: 302,316, 2004. [source] Artificial neural network modeling of O2 separation from air in a hollow fiber membrane moduleASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2008S. S. Madaeni Abstract In this study artificial neural network (ANN) modeling of a hollow fiber membrane module for separation of oxygen from air was conducted. Feed rates, transmembrane pressure, membrane surface area, and membrane permeability for the present constituents in the feed were network input data. Output data were rate of permeate from the membrane, the amount of N2 in the remaining flow, and the amount of O2 in the permeate flow. Experimental data were obtained from software developed by Research Institute of Petroleum Industry (RIPI). A part of the data generated by this software was confirmed by experimental results available in literature. Two third of the data were employed for training ANNs. Based on different training algorithms, radial basis function (RBF) was found as the best network with minimum training error. Generalization capability of best RBF networks was checked by one third of unseen data. The network was able to properly predict new data that incorporate excellent performance of the network. The developed model can be used for optimization and online control. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd. [source] Improving the prediction of liquid back-mixing in trickle-bed reactors using a neural network approachJOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 9 2002Simon Piché Abstract Current correlations aimed at estimating the extent of liquid back-mixing, via an axial dispersion coefficient, in trickle-bed reactors continue to draw doubts on their ability to conveniently represent this important macroscopic parameter. A comprehensive database containing 973 liquid axial dispersion coefficient measurements (DAX) for trickle-bed operation reported in 22 publications between 1958 and 2001 was thus used to assess the convenience of the few available correlations. It was shown that none of the literature correlations was efficient at providing satisfactory predictions of the liquid axial dispersion coefficients. In response, artificial neural network modeling is proposed to improve the broadness and accuracy in predicting the DAX, whether the Piston,Dispersion (PD), Piston,Dispersion,Exchange (PDE) or PDE with intra-particle diffusion model is employed to extract the DAX. A combination of six dimensionless groups and a discrimination code input representing the residence-time distribution models are used to predict the Bodenstein number. The inputs are the liquid Reynolds, Galileo and Eötvos numbers, the gas Galileo number, a wall factor and a mixed Reynolds number involving the gas flow rate effect. The correlation yields an absolute average error (AARE) of 22% for the whole database with a standard deviation on the AARE of 24% and remains in accordance with parametric influences reported in the literature. © 2002 Society of Chemical Industry [source] | ||||||||||