Journal of Operational Research in its Applications
Year:2008 | Volume:5 | Issue:18|Papers Count:9

The choice of the best method estimation method is one of the goals in estimating parameters of the distribution. In this paper, I obtain the maximum likelihood and moment estimators of the two unknown parameters of a burr type XII distribution. Also Bayesian estimator of the parameter p under two different loss functions are derived. It is assumed that the parameter p behaves as a random variable has a gamma prior. Finally, numerical examples are given to illustrate some of theoretical results.

An optimization model with a multi objective function subject to a system of fuzzy relation equations with Yager’s union operator is presented. The solution set of such a fuzzy relation equations is a non-convex set. Thus, the ordinary methods such as simplex or interior point methods don't use for solving these problems. In this paper, we first characterize the feasible solution set and then, optimize a linear objective function. In order to optimize it, we convert it to an equivalent problem involving 0-1 integer programming with a branch-and-bound solution technique. Finally, we optimize a multi objective function with LP metric methods. Furthermore, a concrete example is included for illustration purpose.

One of the systems that be used in order to prove efficiency of control methods with its easy structure, is inverted pendulum system. The object of controlling this system is equilibrium of pendulum with limited states. For this purpose, different methods are used. One of the best methods is fuzzy controller with defuzzy supervisory controller. In almost methods, limitation of initial conditions is important problem. In this paper for solving this problem, a new method is proposed. Because of similarity to back stepping method but its more applications than last method, we named this method to Generalized Back stepping Method.

The linear systems with M and H-matrices as coefficient matrix many often arise in science and engineering. In this article we consider some preconditioners of [12] for solving a linear system with the above coefficient matrix. Also we use preconditioned AOR method for the solution of the systems. The convergence theorems for the preconditioners also presented. A numerical example is given to test the methods.

Forecasting statistical data of waves such as periods, and altitude is important in sea environment. This paper uses the real data set of Caspian Sea’s waves to forecast the altitude of waves. Toward this end, we use neural network analysis.

In Adomian's decomposition method the Taylor series of any function of independent variable in the equation around the initial point of the required interval uses to obtain the solution of differential equation as a polynomial. This causes that the solution become inaccurate at the end of the interval. In this paper two methods are introduced to resolve this problem and are examined through numerical examples.

In this paper, efforts were made in order to use the visual system in controlling an industrial 6R robot and also it is tried that the visual system, realizes the target and controls the robot by taking picture from the environment and processing the picture. To test the action of Robot, Kinematic equation are used. In Direct Kinematics test, the quantities of joint angles are determined. On the basis of the given angles, the location of the end-effector is calculated by the system and via the Transition Matrix. In Inverse Kinematics test, the specifications of the location of the end-effector are accessible. Using Inverse Kinematics equations, the quantity of joint angles are distinguished and the robot rotates to that quantities and on this basis, the location of the end-effector in space becomes distinct. Since the movement of each of the joints has a limited scope and junction of the joints limits their movement scope, the need for knowledge of the working space of a robot seems necessary. To find Singular Points, we seem determinant of the Jacobean matrix equal zero and we determined the working space of the robot by Matlab Simulink. Finally the results are tested within the ANSI-RIA R15.05-2 standard.

In this paper, we show that the unbiased estimator of the certion Parameter of the selected population does not exist. First, we give a new proof of this fact for the selected normal population. We then extend the result to some other distributions belonging to one-parameteric exponential family. Whenever an unbiased estimator exists, it is shown to be a function of order statiscs.