Shiraz UniversityIranian Journal of Science and Technology Transactions of Electrical Engineering2228-617937220131230THE EFFECT OF NON-UNIFORM AIR-GAP ON THE NOISE IN SWITCHED RELUCTANCE MOTORS183191188710.22099/ijste.2013.1887ENJournal Article20130104The major problems in switched reluctance motors (SRMs) are radial force and torque<br />ripple which cause increased undesirable acoustic noise. This paper describes an approach to<br />determine optimum magnetic circuit parameters to minimize both radial force and torque ripple for<br />such motors. There is no publication for simultaneous reduction of both radial force and torque<br />ripple. In previous works, torque ripple was decreased without any research on the radial force or<br />counter. In this paper, a procedure for radial force and torque ripple reduction in SR motors is<br />proposed. To decrease the acoustic noise, the air gap width is increased while the radial force is<br />maximized. On the other hand, by increasing the air gap width, torque decreases. By varying the<br />angular interval and consequently the air gap width, the optimum angular interval is achieved. In<br />the optimum angular interval, the radial force decreases while the torque remains constant. A twodimensional<br />(2-D) finite element (FE) analysis carried out on the 6/4 SRM. By using the method<br />of the compensated current, the ripple torque can be reduced to zero, radial force decreases 3.7%,<br />and the acoustic noise power decreases 7.3% in the non-uniform air gap in comparison with the<br />static case. Radial force decreases 5.6% and the acoustic noise power decreases 10.9% in the<br />uniform air gap in comparison with the static case.http://ijste.shirazu.ac.ir/article_1887_2e97bdd46a60817fde7a45b46b62a41b.pdfShiraz UniversityIranian Journal of Science and Technology Transactions of Electrical Engineering2228-617937220131230COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK193198188810.22099/ijste.2013.1888ENJournal Article20121212In recent years Fuzzy Wavelet Neural Networks (FWNNs) have been used in many<br />areas. Function approximation is an important application of FWNNs. One of the main problems<br />in effective usage of FWNN is tuning of its parameters. In this paper several different evolutionary<br />algorithms including Genetic Algorithm (GA), Gravitational Search Algorithm (GSA),<br />Evolutionary Strategy (ES), Fast Evolutionary Strategy (FES) and variants of Differential<br />Evolutionary algorithms (DE) are used for adjusting these parameters on five test functions. The<br />obtained results are compared based on some measures by using multiple non-parametric<br />statistical tests. The comparison reveals the superiority of some variants of DE in terms of<br />convergence behavior and the ability of function approximation.http://ijste.shirazu.ac.ir/article_1888_72ff271533dd73b4414448587d27c534.pdf