this paper presents an approach for solving a nonlinear stochastic differential equations (nsdes) using a new basis functions (nbfs). these functions and their operational matrices areused for representing matrix form of the nbfs. with using this method in combination with the collocation method, the nsdes are reduced a stochastic nonlinear system of equations and unknowns. then, the error anal...

in this article,we present a wavelet method for solving stochastic volterra integral equations based on haar wavelets. first, we approximate all functions involved in the problem by haar wavelets then, by substituting the obtained approximations in the problem, using the it^{o} integral formula and collocation points then, the main problem changes into a system of linear or nonlinear equation w...

we focus on the use of two stable and accurate explicit finite difference schemes in order to approximate the solution of stochastic partial differential equations of it¨o type, in particular, parabolic equations. the main properties of these deterministic difference methods, i.e., convergence, consistency, and stability, are separately developed for the stochastic cases.

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

Performance evaluation of electricity distribution units is an important issue between researchers and regulators. Classic Data Envelopment Analysis models with deterministic data have been used by many authors to measure efficiency of power distribution units in different countries. However, Data Envelopment Analysis with stochastic data are rarely used to measure efficiency of distribution co...

By adopting on a notion of stochastic differential contraction, the paper presents new results on the incremental mean squared gain (IMSG) analysis of nonlinear systems with stochastic inputs. The relative power between two stochastic processes is defined as the asymptotic average (over time) of the second moment of the point-wise distance (in some metric) between the two processes. The IMSG of...

Turn-based stochastic games and its important subclass Markov decision processes (MDPs) provide models for systems with both probabilistic and nondeterministic behaviors. We consider turnbased stochastic games with two classical quantitative objectives: discounted-sum and long-run average objectives. The game models and the quantitative objectives are widely used in probabilistic verification, ...

The supply of electrical energy is being increasingly sourced from renewable generation resources. The variability and uncertainty of renewable generation, compared to a dispatchable plant, is a significant dissimilarity of concern to the traditionally reliable and robust distribution systems. In order to reach the optimal operation of community Microgrids (MGs) including various Distributed En...

The spatial variation of crop yield was mainly caused by the complicated factors, such as soil and land factors. Based on sound theoretical reasoning, the paper performed an exploratory investigation of the relationship between the spatial distribution of yield measurement about 849 samplings on Thiessen polygon. Reasonable sample size or sampling density is an assurance of yield estimation sur...

Many computer networks such as wireless networks are stochastic in nature. In order to perform performance guarantee analysis of such networks, a theory, called stochastic network calculus, has evolved. In the stochastic network calculus literature, most results are based on space-domain traffic and service models where the arrival process and the service process are respectively characterized ...