The major aim of this study was to model the effect of two causal factors, i.e. coating weight gain and amount of pectin-chitosan in the coating solution on the in vitro release profile of theophylline for bimodal drug delivery. Artificial neural network (ANN) as a multilayer perceptron feedforward network was incorporated for developing a predictive model of the formulations. Five different tr...

In this paper, the Levenberg–Marquardt (LM), Bayesian regularization (BR), resilient backpropagation (RP), gradient descent momentum (GDM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and scaled conjugate (SCG) algorithms constructed using artificial neural networks (ANN) are applied to problem of MPPT energy harvesting in solar photovoltaic (PV) systems for purpose creating a comparative evaluati...

Well logging inversion was carried out using Levenberg-Marquardt (LM) and Singular Value Decomposition (SVD) techniques for the determination of petrophysical parameters, respectively. In this research, synthetic data contaminated with 5% Gaussian noise, field were used to compare results from two methods. MATLAB software has been developed solve overdetermined inverse problem. The estimated pa...

Abstract This paper proposes a scattering area model for processing multipath parameters achieve single base station positioning. First of all, we construct based on the spatial layout obstacles near and then collect signals needed positioning extract parameters. Second, use joint clustering algorithm improved by k-means mean shift to process useful information. Third, processed information is ...

In this study, some physical and mechanical properties of yellow pine wood (Pinus sylvestris), which is used extensively in furniture industry, were tested after heat treatment. The findings obtained modelled by artificial neural network (ANN) interval values related to temperature time variations tried be estimated. This makes it easier reach intermediate values, aims save the relevant researc...

Abstract A new Levenberg–Marquardt (LM) method for solving nonlinear least squares problems with convex constraints is described. Various versions of the LM have been proposed, their main differences being in choice a damping parameter. In this paper, we propose rule updating parameter so as to achieve both global and local convergence even under presence constraint set. The key our results per...

Renewable sources are essential in fulfilling affordable, clean, and sustainable energy requirements. Consistent wind speed forecasting accurate solar radiation prediction required to minimize economic losses enhance the security of power usage. Predicting is difficult due their uncertain behaviour. Motivated by these aspects, this study implemented an Internet Things (IoT) artificial neural ne...

Accurate predictions of radio frequency electromagnetic field (RF-EMF) levels can help implement measures to reduce exposure and check regulatory compliance. Therefore, this study aims predict the RF-EMF in medium using an artificial neural network (ANN). The work was conducted at Ondokuz Mayis University, Kurupelit Campus, where measurement location has line-of-sight base stations. Band select...

Accurate estimation of streamflow is crucial for water resources planning, design and management, determining flood drought management strategies, minimizing their adverse effects. In this study, the usability Artificial Neural Network (ANN) models to estimate monthly was investigated. For purpose, data two stations located in Seyhan Basin south Turkey were used. The Sarız River-Şarköy observat...

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number control variables and dynamic interactions, leading to new grid challenges. For stability analysis designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on accurate estimation parameter calibration DER_A, a recently proposed aggregated model....