Basem E. Elnaghi

A BA-based algorithm for parameter optimization of Support Vector Machine

Abstract Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms.

Meta-Heuristic Algorithm Inspired by Grey Wolves for Solving Function Optimization Problems

In this paper, we suggest the use of Grey Wolf Optimization (GWO) algorithm to solve numerical optimization problems. GWO is compared with two well-known optimization algorithms namely, Bat Algorithm (BA) and Particle Swarm Optimization (PSO), to test the improvement in the accuracy of finding the near optimal solution and the reduction in the computational cost. Ten standard benchmark functions were applied to test the performance of the three optimization algorithms in terms of accuracy and computational cost. The experimental results proved that our proposed method achieved accuracy better than the other two algorithms and it reduced the computational cost and converged rapidly to the optimal solution.

Optimal cross-section of three-phase interior permanent magnet synchronous motor

Permanent magnet synchronous motors have been widely used in many industrial applications. This paper presents an optimization strategy for the cross-section of three-phase interior permanent magnet synchronous motor with maximum motor torque. The optimization strategy includes several steps, stator windings configuration, currents in the stator windings, and the design parameters of the slot tooth in the stator and permanent magnet in the rotor are consequently defined. The optimization strategy results provides the optimized stator and rotor geometrical dimensions and the influence of the air gap variation, are discussed. The optimization calculation has been performed by means of the finite element magnetic method. This strategy leads to the reduction of manufacturing costs for the motor of the same overall dimensions.

Performance of doubly-fed induction generator based wind turbine using adaptive neuro-fuzzy inference system

This paper presents a direct vector control of rotor and grid side converter of doubly-fed induction generator intended to control the generated stator powers based on adaptive neuro-fuzzy inference system. This control system is intended to be implemented in a variable speed wind energy conversion system connected to the grid. In order to control the active and reactive power exchanged between the machine stator and the grid, the rotor is fed by a bi-directional converter. The doubly-fed induction generator is controlled by standard relay controllers. A detail of the control strategy and system simulation was done using SIMULINK. The results are featured to show the effectiveness of the proposed control strategy.

Automatically Human Age Estimation Approach via Two-Dimensional Facial Image Analysis

In this paper, the human age automatically estimated via two-dimensional facial image analysis. The exact age estimation is often treated as a classification problem while it can be formulated as a regression problem. In our research, a classification and regression models are proposed. The two proposed models are evaluated using the same database images and the same features. Due to a big difference between the number of samples in each class or age group, the two proposed models used the complete and missing data in different experiments. Moreover, we compared age estimation errors when (1) Age estimation is performed without discrimination between males and females (gender unknown); (2) Age estimation is performed in males and females separately (gender known). Conclusions and results of these proposed models are shown by extensive experiments on the public available FG-NET database.