The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
N. Sarikaya - One of the best experts on this subject based on the ideXlab platform.
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adaptive network based fuzzy inference system models for Input Resistance computation of circular microstrip antennas
Microwave and Optical Technology Letters, 2008Co-Authors: Kerim Guney, N. SarikayaAbstract:A method based on adaptive-network-based fuzzy inference system (ANFIS) for computing the Input Resistance of circular microstrip antennas (MSAs) is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, genetic, differential evolution, hybrid learning, particle swarm, and simulated annealing, are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results, as compared with the results of previous methods available in the literature. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1253–1261, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23354
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Input Resistance Calculation for Circular Microstrip Antennas Using Adaptive Neuro-Fuzzy Inference System
International Journal of Infrared and Millimeter Waves, 2004Co-Authors: Kerim Guney, N. SarikayaAbstract:This paper presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the Input Resistance of circular microstrip patch antennas. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of FIS with learning power of neural networks. A hybrid learning algorithm based on the least square approach and the backpropagation algorithm is used to optimize the parameters of ANFIS. The Input Resistance results predicted by ANFIS are in excellent agreement with the experimental results reported elsewhere.
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adaptive neuro fuzzy inference system for the Input Resistance computation of rectangular microstrip antennas with thin and thick substrates
Journal of Electromagnetic Waves and Applications, 2004Co-Authors: Kerim Guney, N. SarikayaAbstract:A new method for calculating the Input Resistance of electrically thin and thick rectangular microstrip patch antennas, based on the adaptive neuro-fuzzy inference system (ANFIS), is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The Input Resistance results obtained by using the new method are in very good agreement with the experimental results available in the literature.
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artificial neural networks for calculating the Input Resistance of circular microstrip antennas
Microwave and Optical Technology Letters, 2003Co-Authors: Kerim Guney, N. SarikayaAbstract:A method based on artificial neural networks (ANNs) for calculating the Input Resistance of circular microstrip patch antennas is presented. The Levenberg–Marquardt algorithm is used to train the networks. The theoretical Input Resistance results obtained by using this method are in very good agreement with the experimental results available in the literature. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 107–111, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10838
Mehrdad Moallem - One of the best experts on this subject based on the ideXlab platform.
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bridgeless converter with Input Resistance control for low power energy harvesting applications
Iet Power Electronics, 2015Co-Authors: Chenyu Hsieh, Mehrdad Moallem, Farid GolnaraghiAbstract:In this study, a new switched-mode bridgeless converter operating in the discontinuous conduction mode is presented along with an Input Resistance control scheme. Through feedback control, the converter-battery circuitry synthesises variable line resistor with regenerative capability is intended for large-scale electromagnetic-based energy harvesting systems. For instance, the circuit can operate as a vehicular regenerative damper through converting vibration energy into battery charge. Analysis of the power converter in various switching modes is presented along with implementation of a control method for synthesising a desired Input Resistance. Experimental and simulation results are presented that highlight operations and power efficiencies of the proposed power converter along with its control scheme.
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a low power electronics converter with Input Resistance control for piezoelectric energy harvesting
International Conference on Advanced Intelligent Mechatronics, 2014Co-Authors: S. Faghihi, Mehrdad MoallemAbstract:This work describes the modeling and analysis of a low power electronics converter for piezoelectric energy harvesting. The proposed converter consists of a diode bridge rectifier, a MOSFET switch driven by a PWM signal, and a rechargeable battery as the storage device. The circuit is used to convert mechanical vibration energy into electric charge stored in a battery using piezoelectric transducers mounted on a flexible structure. By utilizing an averaging scheme, it is shown that the converter exhibits a pseudo-resistive behavior across its Input terminals. An analytical expression for the Input Resistance of the converter is derived and further evaluated by simulations and experiments. A self-powered converter is utilized to convert vibration energy into electric charge whose Input Resistance can be set to a prescribed value depending on the Input source Resistance. It is also shown that by applying a feedback controller to the circuit, the Input Resistance of the converter can be regulated to a desired value. This feature may be utilized to achieve impedance matching in applications requiring maximum power transfer. Experimental studies are conducted to verify the performance of the self-powered circuit and the feedback control scheme.
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maximum power point estimation and tracking using power converter Input Resistance control
Solar Energy, 2013Co-Authors: Yaser M Roshan, Mehrdad MoallemAbstract:Abstract In this paper, the idea of controlling the Input Resistance of a switching power converter is proposed to track the maximum power point of a photovoltaic (PV) module. To this end, an inversion-based control technique is presented based on the nonlinear Input Resistance model of a boost converter operating in the discontinuous conduction mode. A method is also presented to estimate the Resistance of the PV module at the maximum power point by means of the Lambert W-Function. Furthermore, the Resistance information is utilized to control the Input Resistance of the converter for achieving maximum power transfer. Simulation and experimental results indicate that the PV system, working under the proposed controller, can successfully track different maximum power points under rapidly changing irradiance and load conditions.
Kerim Guney - One of the best experts on this subject based on the ideXlab platform.
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adaptive network based fuzzy inference system models for Input Resistance computation of circular microstrip antennas
Microwave and Optical Technology Letters, 2008Co-Authors: Kerim Guney, N. SarikayaAbstract:A method based on adaptive-network-based fuzzy inference system (ANFIS) for computing the Input Resistance of circular microstrip antennas (MSAs) is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, genetic, differential evolution, hybrid learning, particle swarm, and simulated annealing, are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results, as compared with the results of previous methods available in the literature. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1253–1261, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23354
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Input Resistance Calculation for Circular Microstrip Antennas Using Adaptive Neuro-Fuzzy Inference System
International Journal of Infrared and Millimeter Waves, 2004Co-Authors: Kerim Guney, N. SarikayaAbstract:This paper presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the Input Resistance of circular microstrip patch antennas. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of FIS with learning power of neural networks. A hybrid learning algorithm based on the least square approach and the backpropagation algorithm is used to optimize the parameters of ANFIS. The Input Resistance results predicted by ANFIS are in excellent agreement with the experimental results reported elsewhere.
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adaptive neuro fuzzy inference system for the Input Resistance computation of rectangular microstrip antennas with thin and thick substrates
Journal of Electromagnetic Waves and Applications, 2004Co-Authors: Kerim Guney, N. SarikayaAbstract:A new method for calculating the Input Resistance of electrically thin and thick rectangular microstrip patch antennas, based on the adaptive neuro-fuzzy inference system (ANFIS), is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The Input Resistance results obtained by using the new method are in very good agreement with the experimental results available in the literature.
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artificial neural networks for calculating the Input Resistance of circular microstrip antennas
Microwave and Optical Technology Letters, 2003Co-Authors: Kerim Guney, N. SarikayaAbstract:A method based on artificial neural networks (ANNs) for calculating the Input Resistance of circular microstrip patch antennas is presented. The Levenberg–Marquardt algorithm is used to train the networks. The theoretical Input Resistance results obtained by using this method are in very good agreement with the experimental results available in the literature. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 107–111, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10838
Andrew Cross - One of the best experts on this subject based on the ideXlab platform.
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negative Input Resistance compensator for a constant power load
IEEE Transactions on Industrial Electronics, 2007Co-Authors: A J Forsyth, Andrew CrossAbstract:A negative Input-Resistance compensator is designed to stabilize a power electronic brushless dc motor drive with constant power-load characteristics. The strategy is to feed a portion of the changes in the dc-link voltage into the current control loop to modify the system Input impedance in the midfrequency range and thereby to damp the Input filter. The design process of the compensator and the selection of parameters are described. The impact of the compensator is examined on the motor-controller performance, and finally, the effectiveness of the controller is verified by simulation and experimental testing.
Yoshihisa Kurachi - One of the best experts on this subject based on the ideXlab platform.
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the endocochlear potential depends on two k diffusion potentials and an electrical barrier in the stria vascularis of the inner ear
Proceedings of the National Academy of Sciences of the United States of America, 2008Co-Authors: Hiroshi Hibino, Yasuo Hisa, Toshihiro Suzuki, Yoshihisa KurachiAbstract:An endocochlear potential (EP) of +80 mV is essential for audition. Although the regulation of K+ concentration ([K+]) in various compartments of the cochlear stria vascularis seems crucial for the formation of the EP, the mechanism remains uncertain. We have used multibarreled electrodes to measure the potential, [K+], and Input Resistance in each compartment of the stria vascularis. The stria faces two fluids, perilymph and endolymph, and contains an extracelluar compartment, the intrastrial space (IS), surrounded by two epithelial layers, the marginal cell (MC) layer and that composed of intermediate and basal cells. Fluid in the IS exhibits a low [K+] and a positive potential, called the intrastrial potential (ISP). We found that the Input Resistance of the IS was high, indicating this space is electrically isolated from the neighboring extracellular fluids. This arrangement is indispensable for maintaining positive ISP. Inhibiting the K+ transporters of the stria by anoxia, ouabain, or bumetanide caused the [K+] of the IS to increase and the intracellular [K+] of MCs to decrease, reducing both the ISP and the EP. Calculations indicate that the ISP represents the K+ diffusion potential across the apical membranes of intermediate cells through Ba2+-sensitive K+ channels. The K+ diffusion potential across the apical membranes of MCs also contributes to the EP. Because the EP depends on two K+ diffusion potentials and an electrical barrier in the stria vascularis, interference with any of these elements can interrupt hearing.
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the endocochlear potential depends on two k diffusion potentials and an electrical barrier in the stria vascularis of the inner ear
Proceedings of Annual Meeting of the Physiological Society of Japan Proceedings of Annual Meeting of the Physiological Society of Japan, 2008Co-Authors: Hiroshi Hibino, Fumiaki Nin, Yoshihisa KurachiAbstract:An endocochlear potential (EP) of 80 mV is essential for audition. Although the regulation of K concentration ((K)) in various compartments of the cochlear stria vascularis seems crucial for the formation of the EP, the mechanism remains uncertain. We have used multibarreled electrodes to measure the potential, (K), and Input Resistance in each compartment of the stria vascularis. The stria faces two fluids, perilymph and endolymph, and contains an extracelluar compartment, the intrastrial space (IS), surrounded by two epithelial layers, the marginal cell (MC) layer and that com- posed of intermediate and basal cells. Fluid in the IS exhibits a low (K) and a positive potential, called the intrastrial potential (ISP). We found that the Input Resistance of the IS was high, indicating this space is electrically isolated from the neighboring extracellular fluids. This arrangement is indispensable for maintaining positive ISP. Inhibiting the K transporters of the stria by anoxia, ouabain, or bumetanide caused the (K) of the IS to increase and the intracellular (K) of MCs to decrease, reducing both the ISP and the EP. Calculations indicate that the ISP represents the K diffusion potential across the apical membranes of intermediate cells through Ba2-sensitive K channels. The K diffusion potential across the apical membranes of MCs also contributes to the EP. Because the EP depends on two K diffusion potentials and an electrical barrier in the stria vascularis, interference with any of these elements can interrupt hearing. hearing ion transport potassium
