AMIRKABIR INTERNATIONAL JOURNAL OF MODELING, IDENTIFICATION, SIMULATION AND CONTROL
Year:2015 | Volume:47 | Issue:1|Papers Count:6

In this paper, we propose a new gene selection algorithm based on Shuffled Frog Leaping Algorithm that is called SFLA-FS. The proposed algorithm is used for improving cancer classification accuracy. Most of the biological datasets such as cancer datasets have a large number of genes and few samples. However, most of these genes are not usable in some tasks for example in cancer classification. Therefore, selection of the appropriate genes is important in bioinformatics and machine learning. The proposed method combines the advantage of wrapper and filter methods for gene subset selection. SFLA-FS consists of two phases. In the first phase a filter method is used for gene ranking from high dimensional microarray data and in the second phase, SFLA is applied to gene selection. The performance of SFLA-FS evaluated for cancer classification using seven standard microarray cancer datasets. Experimental results are compared with those of obtained from several existing well-known gene selection algorithm. The experimental results show that SFLA-FS has a remarkable ability to generate reduced size of genes while yielding significant classification accuracy in cancer classification.

In this paper, a new analytical method to find a near-optimal high gain controller for the non-minimum phase affine nonlinear systems is introduced. This controller is derived based on the closed form solution of the Hamilton-Jacobi-Bellman (HJB) equation associated with the cheap control problem. This methodology employs an algebraic equation with parametric coefficients for the systems with scalar internal dynamics and a differential equation for those systems with the internal dynamics of order higher than one. It is shown that 1) if the system starts from different initial conditions located in the close proximity of the origin the regulation error of the closed-loop system with the proposed controller is less than that of the closed-loop system with the high gain LQR, which is surely designed for the linearized system around the origin, 2). for the initial conditions located in a region far from the origin, the proposed controller significantly outperforms the LQR controller.

A common problem arising in project management is the fact that the baseline schedule is often disrupted during the project execution because of uncertain parameters. As a result, project managers are often unable to meet the deadline time of the milestones. Robust project scheduling is an effective approach in case of uncertainty. Upon adopting this approach, schedules are protected against possible disruptions that may occur during project execution. In order to apply robust scheduling principles to real projects, one should make assumptions close to the actual conditions of the project as much as possible. In this paper, in terms of uncertainty in both activities duration and resources availability, some methods are proposed to construct the robust schedules. In addition, various numerical experiments are applied to different problem types with the aid of simulation. The main purpose of those is to assess the performance of robust scheduling methods under different conditions. Finally, we formulate recommendations regarding the best method of robust scheduling based on the results of these experiments.

In this paper, a recurrent fuzzy-neural network (RFNN) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. Since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command signal and inlet guide vane position. Also practical limitations are applied to system inputs. In addition, demand power and ambient temperature are considered as disturbance. Simulation results show the effectiveness of proposed controller in comparison with other conventional methods such as Model Predictive Control (MPC) and H¥ control in a same operating condition.

Data Grid is an infrastructure that controls huge amount of data files, and provides intensive computational resources across geographically distributed collaboration. The heterogeneity and geographic dispersion of grid resources and applications place some complex problems such as job scheduling. Most existing scheduling algorithms in Grids only focus on one kind of Grid jobs which can be data-intensive or computation-intensive. However, only considering one kind of jobs in scheduling does not result in suitable scheduling in the viewpoint of all systems, and sometimes causes wasting of resources on the other side. To address the challenge of simultaneously considering both kinds of jobs, a new Integrated Job Scheduling Strategy (IJSS) is proposed in this paper. At one hand, the IJSS algorithm considers both data and computational resource availability of the network, and on the other hand, considering the corresponding requirements of each job, it determines a value called W to the job. Using the W value, the importance of two aspects (being data or computation intensive) for each job is determined, and then the job is assigned to the available resources. The simulation results with OptorSim show that IJSS outperforms comparing to the existing algorithms mentioned in literature as number of jobs increases.

In a strap down magnetic compass, heading angle is estimated using the Earth's magnetic field measured by Three-Axis Magnetometers (TAM). However, due to several inevitable errors in the magnetic system, such as sensitivity errors, non-orthogonal and misalignment errors, hard iron and soft iron errors, measurement noises and local magnetic fields, there are large error between the magnetometers' outputs and actual geomagnetic field vector. This is the necessity of magnetic calibration of TAM, especially in navigation application to achieve the true heading angle. In this paper, two methodologies, including clustering swinging method and clustering velocity vector method are presented for magnetic compass calibration. Several factors for clustering process have been introduced and analyzed. The algorithms can be applied in both low-cost MEMS magnetometer and high-accuracy magnetic sensors. The proposed calibration algorithms have been evaluated using in-ground and in-flight tests. It can be concluded from the experimental results that, applying the clustering calibration algorithms bring about a considerable enhancement in the accuracy of magnetic heading angle.