Nurhan Turker Tokan

KNOWLEDGE-BASED SUPPORT VECTOR SYNTHESIS OF THE MICROSTRIP LINES

In this paper, we proposed an efficient knowledge-based Support Vector Regression Machine (SVRM) method and applied it to the synthesis of the transmission lines for the microwave integrated circuits, with the highest possible accuracy using the fewest accurate data. The technique has integrated advanced concepts of SVM and knowledge-based modeling into a powerful and systematic framework. Thus, synthesis model as fast as the coarse models and at the same time as accurate as the fine models is obtained for the RF/Microwave planar transmission lines. The proposed knowledge-based support vector method is demonstrated by a typical worked example of microstrip line. Success of the method and performance of the resulted synthesis model is presented and compared with ANN results.

A knowledge-based support vector synthesis of the transmission lines for use in microwave integrated circuits

In this paper, we proposed an efficient knowledge-based support vector regression machine (SVRM) method to build synthesis models of the transmission lines for the microwave integrated circuits, with the highest possible accuracy using the fewest accurate data. This method is based comprehensively on the powerful generalization capability of support vector machine (SVM) over other classical optimization techniques; especially its working principle based on the small sample statistical learning theory is utilized in lessening the need for the accurate training and validation data together with the human time. Thus, synthesis models as fast as the coarse models and at the same time as accurate as the fine models are obtained for the RF/microwave planar transmission lines. Since the method employs the reverse relations between the analysis and synthesis processes, therefore firstly general definitions of analysis and synthesis processes are made for the RF/microwave planar transmission lines. Then the synthesis data are obtained by reversing the analysis data according to these definitions, where analysis process may be based on either the analytical formulation or empirical (coarse) formulas. Thereafter, generation process of the fine support vector (SV) expansion for synthesis from the coarse SVs is put forward in the form of block diagrams, depending on type of the analysis processes. Finally, the proposed knowledge-based support vector method are demonstrated by the two typical worked examples, representing the typical analysis processes which belong to the commonly used transmission lines, conductor backed coplanar waveguides with upper shielding and microstrip lines. Besides, artificial neural network (ANN)s are employed also in modeling as a competent regressor and it is also verified that only SVs would be sufficient to be used in training ANN models. Success of the method and performances of the resulted synthesis models are presented as compared to each other and the conventional ones.

The planar lateral wave antenna

A novel planar lateral wave antenna concept that promises impedance matching over factor a one to three bandwidth, very high efficiency, and high directivity in one plane in presented. The structure realizes a planar elliptical dielectric lens antenna for focusing the beams in the H-plane. A broad band feed launches wave within a parallel plate waveguide region, filled with dense dielectric. These waves realize a directive and almost frequency independent aperture field configuration, exploiting a novel lateral wave propagation mechanism.

The Lateral Wave Antenna

This paper introduces a quasi-planar antenna that provides, over a broad frequency bandwidth, highly directive radiation in one plane, phase center stability as a function of the frequency and high efficiency. The structure is composed by a dielectrically filled parallel plate waveguide that supports the propagation of a transverse electromagnetic (TEM) wave. The waveguide is truncated and shaped as a planar lens to achieve directive beams in the H-plane, while a short flaring is added at the waveguide open end to facilitate radiation into free space. High efficiency is achieved thanks to the peculiar feed structure that supports the propagation of directive lateral waves. An antenna prototype has been manufactured and measured. The results highlight the highest directivity for a quasi-planar broadband antenna (3:1 bandwidth) with very low dispersion and frequency-stable phase center.

Analysis and Synthesis of the Microstrip Lines Based on Support Vector Regression

In this work, the support vector regression is adopted to the analysis and synthesis of microstrip lines on all isotropic/anisotropic dielectric materials, which is a novel technique based on the rigorous mathematical fundamentals and the most competitive technique to the popular artificial neural networks. In this design process, accuracy, computational efficiency and number of support vectors are investigated in detail and the support vector regression performance is compared to an artificial neural network performance. It can be concluded that the artificial neural network may be replaced by the support vector machines in the regression applications due to its high approximation capability and much faster convergence rate with the sparse solution technique. Synthesis is achieved by utilizing the analysis black-box bidirectionally by reverse training. Furthermore, by using the adaptive step size, a much faster convergence rate is obtained in the reverse training. Besides, design of microstrip lines on the most commonly used isotropic/anisotropic dielectric materials are given as the worked examples.

Support vector design of the microstrip antenna

In this work, support vector machine (SVM) formulation based upon ldquoNrdquo measured data is worked out for the resonant frequency, operation bandwidth, input impedance of a rectangular microstrip antenna. Results of the formulation are compared with the theoretical results obtained in literature, much better characterisation is observed with greater accuracy. At the same time, artificial neural network (ANN) is employed in generalization of the data on the resonant frequency, operation bandwidth, input impedance of the antenna. Performances of the two advanced nonlinear learning machines are compared and superiority of the SVM is verified.