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Journal of Optoelectronical Nanostructures، جلد ۷، شماره ۳، صفحات ۹۲-۱۰۷
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عنوان انگلیسی Data-Driven State Estimation of Carbon Nanotube Field Effect Transistor with Smart RBF Network
چکیده انگلیسی مقاله Since 1993, Devices based on CNTs have applicationsranging from nanoelectronics to optoelectronics. Thechallenging issue in designing these devices is that thenonequilibrium Green's function (NEGF) method has tobe employed to solve the Schrödinger and Poissonequations, which is complex and time consuming. In thepresent study, a novel smart and optimal algorithm ispresented for fast and accurate modeling of CNT fieldeffecttransistors (CNTFETs) based on an artificial neuralnetwork. A new and efficient way is presented forincrementally constructing radial basis function (RBF)networks with optimized neuron radii to obtain theestimator network. An incremental extreme learningmachine (I-ELM) algorithm is used to train the RBFnetwork. To ensure the optimal radii for incrementalneurons, this algorithm utilizes a modified version of anoptimization algorithm known as the Nelder-Meadsimplex algorithm. Results confirm that the proposedapproach reduces the network size for faster errorconvergence while preserving the estimation accuracy.
کلیدواژه‌های انگلیسی مقاله CNTFET, Modeling, nonlinear system, RBF, state estimation
نویسندگان مقاله Hossein Afkhami |
Department of Mechanical, Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Faridoon Shabani Nia |
Department of Power and Control Engineering, Shiraz University, Shiraz, Iran

Jamshid Aghaei |
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

نشانی اینترنتی https://jopn.marvdasht.iau.ir/article_5340_6867be65cd854ce5a8f76f37121dc0a6.pdf
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