This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years. The purpose of this research is to provide an exhaustive overview of the existing literature which may assist prospective researchers. The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors. The overall trend of the publications in this area of research issued within the last decade is also addressed. The existing body of research has been analyzed and new research directions have been outlined that have been previously ignored. It is expected that researchers across the globe may thus be encouraged to re-direct their attention and resources in order to keep on searching for an optimum solution.
Recently, there has been a significant emphasis in the forecasting of the electricity demand due to the increase in the power consumption. Energy demand forecasting is a very important task in the electric power distribution system to enable appropriate planning for future power generation. Quantitative and qualitative methods have been utilizedpreviously for the electricity demand forecasting. Due to the limitations inthe availability of data, these methods fail to provide effective results. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. This paper presents the computational modeling of electricity consumption based on the Neural Network (NN) training algorithms. The main aim of the work is to determine the optimal training algorithm for electricity demand forecasting. From the experimental analysis, it is concluded that the Bayesian regularization training algorithm exhibits low relative error and high correlation coefficient than other training algorithms. Thus, the Bayesian Regularization training algorithm is selected as the optimal training algorithm for the effective prediction of the electricity demand. Finally, the economic input attributes are forecasted for next 15 years using time series forecasting. Using this forecasted economic attributes and with the optimal Bayesian Regularization training algorithm, the electricity demand for the next 15 years ispredicted. The comparative analysis of the NN training algorithms for the proposed dataset and larger datasets obtained from the UCI repository and American Statistical Association shows that the Bayesian Regularization training algorithm yields higher correlation value and lower relative error than other training algorithms.
Using the STDP rule with metaplasticity, we show that to evoke long-term depression (LTD) or depotentiation of synaptic weights in the spiking model of granule cell is not easy. This is in accordance with a number of experimental studies. On the other hand, heterosynaptic LTD which accompanies homosynaptic long-term potentiation (LTP) is induced readily both in the model as well as in experiments. We offer possible explanation of these phenomena from STDP, metaplasticity and spontaneous activity. We suggest conditions under which it would be possible to induce homosynaptic LTD and depotentiation.
The paper presents the results of numerical solution of the evolution law for the constrained mean-curvature flow. This law originates in the theory of phase transitions for crystalline materials and describes the evolution of closed embedded curves with constant enclosed area. It is reformulated by means of the direct method into the system of degenerate parabolic partial differential equations for the curve parametrization. This system is solved numerically and several computational studies are presented as well.
The paper presents the results of numerical solution of the Allen-Cahn equation with a non-local term. This equation originally mentioned by Rubinstein and Sternberg in 1992 is related to the mean-curvature flow with the constraint of constant volume enclosed by the evolving curve. We study this motion approximately by the mentioned PDE, generalize the problem by including anisotropy and discuss the computational results obtained.
We deal with numerical computation of the nonlinear partial differential equations (PDEs) of Black-Scholes type which incorporate the effect of transaction costs. Our proposed technique surmounts the difficulty of infinite domains and unbounded values of the solutions. Numerical implementation shows the validity of our scheme.
The article introduces a new technique for nonlinear system modeling. This approach, in comparison to its alternatives, is straight and computationally undemanding. The article employs the fact that once a nonlinear problem is modeled by a piecewise-linear model, it can be solved by many efficient techniques. Thus, the result of introduced technique provides a set of linear equations. Each of the equations is valid in some region of state space and together, they approximate the whole nonlinear problem. The technique is comprehensively described and its advantages are demonstrated on an example.
The characterization of the solution set of a convex constrained problem is a well-known attempt. In this paper, we focus on the minimum norm solution of a specific constrained convex nonlinear problem and reformulate this problem as an unconstrained minimization problem by using the alternative theorem.The objective function of this problem is piecewise quadratic, convex, and once differentiable. To minimize this function, we will provide a new Newton-type method with global convergence properties.
The numerical range of an n × n matrix is determined by an n degree hyperbolic ternary form. Helton-Vinnikov confirmed conversely that an n degree hyperbolic ternary form admits a symmetric determinantal representation. We determine the types of Riemann theta functions appearing in the Helton-Vinnikov formula for the real symmetric determinantal representation of hyperbolic forms for the genus g = 1. We reformulate the Fiedler-Helton-Vinnikov formulae for the genus g = 0, 1, and present an elementary computation of the reformulation. Several examples are provided for computing the real symmetric matrices using the reformulation., Mao-Ting Chien, Hiroshi Nakazato., and Obsahuje seznam literatury
First, we give complete description of the comultiplication modules over a Dedekind domain. Second, if $R$ is the pullback of two local Dedekind domains, then we classify all indecomposable comultiplication $R$-modules and establish a connection between the comultiplication modules and the pure-injective modules over such domains.