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A new model is derived to predict the peak ground acceleration (PGA) utilizing a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called SA-ANN.
A new nonlinear model was developed to predict the daily solar radiation on horizontal surface using a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA.
In this study, new models are derived to predict the peak time-domain characteristics of strong ground-motions utilizing a novel hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA.
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Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN GA), and simulated annealing (SA) methods (ANN SA), were utilised.
In this paper, the design and modification of hapten (e.g. the choice of modified sites, preparation of hapten derivative, choice of linking spacer), selection of carrier, coupling of hapten derivative to carrier (including coupling method, coupling conditions, the optimal coupling rate), purification and identification of artificial antigens are reviewed in detail.
A coupled artificial neural networks (ANN) and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters.
A method that couples artificial neural network (ANN), genetic algorithm (GA) and computational fluid mechanics (CFD) was proposed to inversely optimize the geometric configuration of exhaust hood and operation parameters.
The pressure and velocities are coupled by artificial compressibility method and the NS equations are solved by pseudotime method with an explicit four-step Runge Kutta integrator.
Moreover, the DM and ML methods are equally useful for big data reduction when coupled with artificial intelligence-based optimization methods.
The method was created using hybrid machine learning approach, namely Genetic algorithm (GA) coupled with artificial neutral networks (ANN).
In this article the application of design of experiment coupled with artificial neural networks in kinetic study of CO hydrogenation reaction and a method of data collection/fitting for the experiment are presented.
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