WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
Browse
2 results
Search Results
Article Citation - WoS: 1New Modeling of Reconfigurable Microstrip Antenna Using Hybrid Structure of Simulation Driven and Knowledge Based Artificial Neural Networks(Pamukkale Univ, 2020) Aoad, Ashrf; Aydin, ZaferKnowledge-based modeling has a critical role to embed existing knowledge to improve modeling performance. Since reconfigurable antenna can provide more operational frequencies than the classical antennas, a knowledge-based hybrid structure is used in this work to obtain efficient model and producing optimum new models for a reconfigurable microstrip antenna. The hybrid structure consists of two phases. The first phase generates initial knowledge which is used in knowledge-based modeling structure to obtain design parameters. Artificial neural network based multilayer perceptron can generate necessary knowledge for a knowledge-based model after the training process. Knowledge-based modeling improves the accuracy of the initial model to determine design parameters corresponding to the design target. Source difference, prior knowledge Input and prior knowledge input with difference can be applied to realize an efficient knowledge-based strategy. 3D-EM simulation generates the new model in terms of the design parameters of the proposed application. It has three switching states for operating, which are organized by two resistor circuits representing ON/OFF states. Switch positions and geometrical parameters can be used for satisfying design targets between 1 GHz and 6 GHz for the efficient antenna design.Conference Object Citation - Scopus: 5Development of Knowledge Based Response Correction for a Reconfigurable N-Shaped Microstrip Antenna Design(Institute of Electrical and Electronics Engineers Inc., 2015-08) Aoad, Ashrf; Simsek, Murat; Aydin, ZaferThis study presents the use of prior knowledge of inverse artificial neural network (ANN) to model and optimize a reconfigurable N-shaped microstrip antenna. Three accurate prior knowledge inverse ANNs with large amount training data are proposed where the frequency information is incorporated into the structure of ANN. The complexity of the input/output relationship is reduced by using prior knowledge. Three separate methods of incorporating knowledge in the second step of the training process with a multilayer perceptron (MLP) in the first step are demonstrated and their results are compared to EM simulation. © 2023 Elsevier B.V., All rights reserved.