Estimation of thermally stimulated current in as grown TlGaSeS layered single crystals by multilayered perceptron neural network

Date

2011-6

Author

Kucuk, Ilker
Yildirim, Tacettin
Hasanlı, Nızamı
Ozkan, Husnu

Metadata

Show full item record

Item Usage Stats

301
views

0
downloads

This paper presents an artificial neural network approach to compute thermally stimulated current (TSC) in as-grown T1GaSeS layered single crystals. The experimental data have been obtained from TSC measurements. The network has been trained by a genetic algorithm (GA). The results confirmed that the proposed model could provide an accurate computation of the TSC.

Subject Keywords

Semiconductors, Thermally stimulated current, Neural network, Genetic algorithm

URI

https://hdl.handle.net/11511/28439

Journal

Expert Systems with Applications

DOI

https://doi.org/10.1016/j.eswa.2010.12.040

Collections

Department of Physics, Article

Suggestions

OpenMETU
Core

Representing temporal knowledge in connectionist expert systems Alpaslan, Ferda Nur (1996-09-27) This paper introduces a new temporal neural networks model which can be used in connectionist expert systems. Also, a Variation of backpropagation algorithm, called the temporal feedforward backpropagation algorithm is introduced as a method for training the neural network. The algorithm was tested using training examples extracted from a medical expert system. A series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The experiments indicated that the alg...
Design and optimization of nanooptical couplers based on photonic crystals involving dielectric rods of varying lengths Yazar, Şirin; Ergül, Özgür Salih (2022-1-01) This study presents design and optimization of compact and efficient nanooptical couplers involving photonic crystals. Nanooptical couplers that have single and double input ports are designed to obtain efficient transmission of electromagnetic waves in desired directions. In addition, these nanooptical couplers are cascaded by adding one after another to realize electromagnetic transmission systems. In the design and optimization of all these nanooptical couplers, the multilevel fast multipole algorithm, w...
Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites Kockan, Umit; Ozturk, Fahrettin; Evis, Zafer (2014-01-01) In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted ...
Neural network prediction of thermophilic (65 degrees C) sulfidogenic fluidized-red reactor performance for the treatment of metal-containing wastewater Sahinkaya, Erkan; Ozkaya, Bestamin; Kaksonen, Anna H.; Puhakka, Jaakko A. (2007-07-01) The performance of a fluidized-bed reactor (FBR) based sulfate reducing bioprocess was predicted using artificial neural network (ANN). The FBR was operated at high (65 degrees C) temperature and it was fed with iron (40-90 mg/ L) and sulfate (1,000-1,500 mg/L) containing acidic (pH = 3.5-6) synthetic wastewater. Ethanol was supplemented as carbon and electron source for sulfate reducing bacteria (SRB). The wastewater pH of 4.3-4.4 was neutralized by the alkalinity produced in acetate oxidation and the aver...
Estimation of protein secondary structure from FTIR spectra using neural networks Severcan, M; Severcan, Feride; Haris, PI (Elsevier BV, 2001-05-30) Secondary structure of proteins have been predicted using neural networks (NN) from their Fourier transform infrared spectra. Leave-one-out approach has been used to demonstrate the applicability of the method. A form of cross-validation is used to train NN to prevent the overfitting problem. Multiple neural network outputs are averaged to reduce the variance of predictions. The networks realized have been tested and rms errors of 7.7% for alpha -helix, 6.4% for beta -sheet and 4.8% for turns have been achi...

Citation Formats

I. Kucuk, T. Yildirim, N. Hasanlı, and H. Ozkan, “Estimation of thermally stimulated current in as grown TlGaSeS layered single crystals by multilayered perceptron neural network,” Expert Systems with Applications, pp. 7192–7194, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/28439.