Yavuz Cengiz

Linear Antenna Array Design with Use of Genetic, Memetic and Tabu Search Optimization Algorithms

Antenna array design techniques are focused on two main classes: uniformly spaced antenna arrays and the non-uniform spacing case. These include techniques based on mathematical programming, such as constrained programming and non-linear programming. More recently, meta-heuristics approaches have been successful at designing antenna arrays (5). In this work, this paper presents efficient methods of genetic algorithm (GA), memetic algorithm (MA) and tabu search algorithm (TSA) for the synthesis of linear antenna design. We present three examples of antenna array design to compare the efficiency of the algorithms through simple design to complex design. The GA, TSA and MA has been used to optimize the spacings between the elements of the linear array to produce a radiation pattern with minimum SLL and null placement control.

Design of a broadband microwave amplifier using neural performance data sheets and very fast simulated reannealing

In this work, the neural performance data sheets of the transistor are employed to determine the feasible design target space in the optimization of a microwave amplifier. The basic amplifier with a single transistor between the input and output matching circuits (IMC and OMC) is also worked out. Very Fast Simulated Reannealing (VFSR) is utilized in the multi – objective optimization process for the global minimum of the objective function which is expressed as a function only gain of a matching circuit, in the negative exponential form to ensure the rapid convergence.

The prediction of the electric field level in the reverberation chamber depending on position of stirrer

This study presents nine modeling techniques within data mining process for the prediction of electric field level which depends on the positions of stirrer in the mode stirred reverberation chamber. These are Linear Regression (LR), Multi Layer Perceptron (MLP), Pace Regression (PR), KStar, Regression by discretization (RD), Random SubSpace (RS), M5P, RepTree and Decision Table models. Relations depending on frequency and position of stirrer have been carried out for the determination of electric field in the reverberation chamber. Obtained model results for each stirrer steps have been compared and the best model has been investigated. Results indicate that the use of derived formulation from these techniques will facilitate design and optimize of location of maxima (mode) and minima (null) of electric field in the reverberation chamber which a component of especially mode stirrer reverberation chamber test method.

Fitting Single Particle Energies in sdgh Major Shell

We have performed two kinds of non-linear fitting procedures to the single-particle energies in the

General regression neural network approach to prediction of electric field level in the reverberation chamber

sdgh

Data mining models for computing the characteristic impedances of elliptic and circular shaped microshield lines

major shell to obtain better shell model results. The low-lying energy eigenvalues of the light Sn isotopes with

Modified Bat Algorithm

A=103-110

Memetic optimization algorithm applied to design microwave amplifier for the specific gain value constrained by the minimum noise over the available bandwidth

in the

Application of data mining methods to efficient microwave active device modeling

sdgh

Pareto-optimal synthesis of microwave amplifier to design the noise-constrained gain value

-shell are calculated in the framework of the nuclear shell model by using CD-Bonn two-body effective nucleon-nucleon interaction. The obtained energy eigenvalues are fitted to the corresponding experimental values by using two different non-linear fitting procedures, i.e., downhill simplex method and clonal selection method. The unknown single-particle energies of the states