Artificial Neural Networks

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.

Artificial neural networks approach in evapotranspiration modeling

Not AvailableAbstract The use of artificial neural networks (ANNs) in estimation of evapotranspiration has received enormous interest in the present decade. Several methodologies have been reported in the literature to realize the ANN modeling of evapotranspiration process. The present review discusses these methodologies including ANN architecture development, selection of training algorithm, and performance criteria. The paper also discusses the future research needs in ANN modeling of evapotranspiration to establish this methodology as an alternative to the existing methods of evapotranspiration estimationNot Availabl

Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans

The results presented in this study include the prediction of the antifungal activity of 24 oxazolo derivatives based on their topological and electrostatic molecular descriptors, derived from the 2D molecular structures. The artificial neural network (ANN) method was applied as a regression tool. The input data for ANN modeling were selected by stepwise selection (SS) procedure. The ANN modeling resulted in three networks with the outstanding statistical characteristics. High predictivity of the established networks was confirmed by comparisons of the predicted and experimental data and by the residuals analysis. The obtained results indicate the usefulness of the formed ANNs in precise prediction of minimum inhibitory concentrations of the analyzed compounds towards Candida albicans. The Sum of Ranking Differences (SRD) method was used in this study to reveal possible grouping of the compounds in the space of the variables used in ANN modeling. The obtained results can be considered to be a contribution to development of new antifungal drugs structurally based on oxazole core, particularly nowadays when there is a lack of highly efficient antimycotics

Application of artificial neural network for prediction of halogenated refrigerants vapor pressure

Application of Artificial Neural Network (ANN) for modeling of vapor pressure for some halogenated refrigerants (halogenated methanes and ethanes) is presented. Neural network training structure was feed forward with back-propagation algorithm. The optimized number of hidden layer and neurons between layers were determined by a trial and error procedure. Neural network parameters were obtained through a learning phase by Levenberg-Marquardt algorithm. The vapor pressure at different temperatures obtained from open literatures was considered as the neural model target. ANN predictions of vapor pressure are more accurate for a wider range of temperature. The ANN modeling reduced the average error for the refrigerants from 0.69% to 0.31% for low temperature range and from 1.39% to 0.99% for high temperature range. Finally, ANN modeling reduced the average error in comparison to theAntoine equation by 47.88% and 32.18% for low and high temperature range, respectively

Methane-Carbon Dioxide: Conversions to Syngas and Hydrocarbons

This monograph describes the new innovation that has recently been developed for the CH4-CO2 conversions process. Optimization of CO2 reforming of methane to synthesis gas with the help of experimental design, empirical modeling and ANN modeling are developed for CORM in presence of oxygen. An overview on dynamic equilibrium analysis has shown that an increase of sweep factors induced more significant enhancement hydrogen permeation than permselective area. The NiO/CeO2 catalyst showed potential as catalyst for the CORM. The application of a hybrid catalytic DBD plasma reactor has the potential for the co-generation of C2+ hydrocarbons and synthesis gases from methane and carbon dioxide. Carbon dioxide as co-feed has important effects on the carbon suppression. It can be concluded that three factors, i.e. CH4/CO2 feed ratio, total feed flow rate, and discharge voltage, in the DBD plasma reactor system have significant effects on the reactor performance. The hybrid catalytic DBD plasma reactor is more suitable for CO2 OCM process than the conventional catalytic reactor over CaO-MnO/CeO2 catalyst. Further innovation and improvement of current research on CH4 and CO2 are required to increase conversion and selectivity and to commercialize the process

ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

This paper discusses the elimination of Colour Index Acid Yellow 23 (C.I. AY23) using the ultraviolet (UV)/Ag-TiO2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO2 nanoparticles processed under desired circumstances, two computational techniques, namely artificial neural network (ANN) and imperialist competitive algorithm (ICA) modeling are developed. A sum of 100 datasets are used to establish the models, wherein the introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO2, and irradiation time are the four parameters expressed in the form of input variables. Additionally, the elimination of AY23 is considered in the form of the output variable. Out of the 100 datasets, 80 are utilized in order to train the models. The remaining 20 that were not included in the training are used in order to test the models. The comparison of the predicted outcomes extracted from the suggested models and the data obtained from the experimental analysis validates that the performance of the ANN scheme is comparatively sophisticated when compared with the ICA scheme

ANN modeling of nickel base super alloys for time dependent deformation

Alloys 617 and 276 are nickel-based super alloys with excellent mechanical properties, oxidation, creepresistance, and phase stability at high temperatures. These alloys are used in complex and stochastic applications. Thus, it is difficult to predict their output characteristics mathematically. Therefore, the non-conventional methods for modeling become more effective. These two alloys have been subjected to time-dependent deformation at high temperatures under sustained loading of different values. The creep results have been used to develop the new models. Artificial neural network (ANN) was applied to predict the creep rate and the anelastic elongation for the two alloys. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed results which indicates the validity of the models

Modeling and Simulation of Single-Phase Transformer Inrush Current using Neural Network

Inrush current is a transient phenomenon which occurs during energization in transformer at no load. It depends on winding impedance, time constant of transformer circuit and core magnetization characteristics. Transient phenomenon of current represents non linear characteristics due to BH curve. Transformer circuit at no load is used to obtain various data. Data is obtained using semi – analytic solution approach. These data is used to develop neural network. Neural network shows exact modeling of inrush current. Keywords: Inrush Current, ANN, Modeling

Optimization of Medium Components Using Artificial Neural Networks

Background: Achieving high cell density is an important goal in recombinant proteins production. Optimization of medium components to achieve high cell density and consequently high yield recombinant protein is a common practice in the biotechnology industry. We could not find an article that just examine the effects of salt on growht transformed BL21. On the other hand, salt is a critical component of medium that can be made up in a medium optimization.Methods: Here, we separately investigated effect of K2HPO4, MgSO4, (NH4)2SO4 and NH4CL on maximum growth of bacteria BL21 after transforming BL21 with PET-32α that containing para thyroid hormones gene. Then, the salts were combined and added to the culture medium for optimization of their effects on high cell density using artificial neural network modelling (ANNs).Results: After ANN modeling, the obtained model showed that MgSO4 has dominant on high cell density other than salts if final concentration of MgSO4 is 25mg/ml. The best concentration each of salt be lower 30 mg/ml and critical total concentration of slats is 120 mg/ml that inhibitory effect was seen after a critical concentration.Conclusions: In current study, ANN modeling shows that in prediction of effects of salts (i.e. K2HPO4, MgSO4, (NH4)2SO4 and NH4CL) on cell density to reach high cell density, is effective and efficient.