نشریه پژوهشهای ریاضی
پیاپی 1 (بهار و تابستان 1394)

One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. Then the bootstrap samples are generated by resampling from the residuals. In this paper, first these sieve bootstrap methods are defined and, then, in a simulation study sieve bootstrap prediction intervals are compared with a standard Gaussian prediction interval. Finally, these methods are used to find the prediction intervals for weather data of Isfahan.

In this paper we introduce a word-based stream cipher consisting of a chaotic part operating as a chaotic permutation and a linear part, both of which designed on a finite field. We will show that this system can operate in both synchronized and self-synchronized modes. More specifically, we show that in the self-synchronized mode the stream cipher has a receiver operating as an unknown input observer. In addition, we evaluate the statistical uniformity of the output and also show that the system in the self-synchronized mode is much faster and lighter for implementation compared to similar self-synchronized systems with equal key size.

Diffusion Processes such as Brownian motions and Ornstein-Uhlenbeck processes are the classes of stochastic processes that have been investigated by researchers in various disciplines including biological sciences. It is usually assumed that the outcomes of these processes are laid on the Euclidean spaces. However, some data in physical, chemical and biological phenomena indicate that they cannot be considered as the observations in Euclidean spaces due to the various features such as the periodicity of the data. Hence, we cannot analyze them using the common mathematical methods available in Euclidean spaces. In addition, studying and analyzing them using common linear statistics are not possible. One of these typical data is the dihedral angles that are utilized in identifying, modeling and predicting the proteins backbones. Because these angles are representatives of points on the surface of torus, it seems that proper statistical modeling of diffusion processes on the torus could be of a great help for the research activities on dynamic molecular simulations in predicting the proteins backbones. In this article, using the Riemannian distance on the torus, the stochastic differential equations to describe the Brownian motions and Ornstein-Uhlenbeck processes on this geometrical object were derived. Then, in order to evaluate the proposed models, the statistical simulations were performed using the equilibrium distributions of aforementioned stochastic processes. Moreover, the link between the gained results with the available concepts in the non-linear statistics were highlighted.

Zobov’s Theorem is one of the theorems which indicate the conditions for the stability of a nonlinear system with specific attraction region. We have applied neural networks to approximate some functions mentioned in Zobov’s theorem in order to find the controller of a nonlinear controlled system whose law in a mathematical manner is difficult to make. Finally, the effectiveness and the applicability of the proposed method are demonstrated through using numerical examples.

Fractional derivatives and integrals are new concepts of derivatives and integrals of arbitrary order. Partial differential equations whose derivatives can be of fractional order are called fractional partial differential equations (FPDEs). Recently, these equations have received special attention due to their high practical applications. In this paper, we survey a rather general case of FPDE to obtain a numerical scheme. The fractional derivatives in the equation are replaced by common definitions such as Grundwald-Letnikov, Riemann-Liouville and Caputo. To improve the numerical solution, partial derivatives inside the equation are discrete using non-standard finite difference scheme. Then, we survey the stability of numerical scheme and prove that the proposed method is unconditionally stable. Eventually, in order to approve the theoretical results, we use the presented technique to solve wave equation with fractional-order, which is very practical and widely used in physics and its branches. Numerical results confirm the findings of the theory and show that this technique is effective.

A new technique to find the optimization parameter in TSVD regularization method is based on a curve which is drawn against the residual norm [5]. Since the TSVD regularization is a method with discrete regularization parameter, then the above-mentioned curve is also discrete. In this paper we present a mathematical analysis of this curve, showing that the curve has L-shaped path very similar to that of the classical L-curve and its corner point can represent the optimization regularization parameter very well. In order to find the corner point of the L-curve (optimization parameter), two methods are applied: pruning and triangle. Numerical results show that in the considered test problems the new curve is better than the classical L-curve.

The link between geographic information systems and decision making approach own the invention and development of spatial data melding method. These methods combine different data sets, to achieve better results. In this paper, the Bayesian melding method for combining the measurements and outputs of deterministic models and kriging are considered. Then the ozone data in Tehran city are analyzed by using these methods. Next their results are evaluated and compared in terms of mean square error criterion. Finally, discussion and results are given.