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    Inverse modeling of pseudo-interdigital bandpass filters using artificial neural networks
    (2013) Demircioğlu, Erdem; Sazlı, Murat H.; Şengül, Orhan; İmeci, Ş. Taha; Gökten, Mesut
    A neural network trained to model original EM problems can be called as the forward model where the model inputs are physical or geometrical parameters and outputs are electrical parameters. Conversely neural network techniques are applicable to inverse modeling of microwave circuit design. In opposition to conventional statistical electromagnetic signal processing applications, inverse modeling techniques acquire electrical parameters as model input and geometrical properties as the output. Pseudo-interdigital (PID) bandpass microstrip filters offer compact and planar solutions to wide bandwidth filtering applications. They avoid the through vias required for short circuiting in conventional interdigital filters. Miniaturized microstrip bandpass filters are in demand for systems requiring small size and light weight. The coupling of the resonators in filter design must be adjusted using EM simulators. There are no analytical or numerical methods proposed for accurate determination of resonator spacing. In this study, the inverse modeling is applied to accurately determine the resonators' locations consistent with desired filter specifications.
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    Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems
    (Hindawi Publishing Corporation, 2015) Demircioğlu, Erdem; Yağli, Ahmet Fazıl; Gülgönül, Şenol; Ankishan, Haydar; Tartan, Emre Oner; Sazlı, Murat H.; İmeci, Ş. Taha
    This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs) using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and Differential Evolution (DE) are applied with ANN's conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking. © 2015 Erdem Demircioglu et al.
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    Soft computing techniques on multiresonant antenna synthesis and analysis
    (2013) Demircioğlu, Erdem; Sazlı, Murat H.; İmeci, Ş. Taha; Şengül, Orhan
    The synthesis and analysis of a multiresonant microstrip patch antenna using soft computing techniques are presented. The multiresonance is obtained via attaching inverted L-shaped stubs to the radiated edges of the single frequency patch antenna. The physical geometry of the proposed antenna is synthesized using adaptive-neuro-fuzzy inference systems and the calculated dimensions are applied to the artificial neural network for the analysis process. The return loss and phase of the scattering parameters are computed. The modeled antenna provides 95% accuracy and sufficient results compared with the simulation and measurement results. © 2013 Wiley Periodicals, Inc.