Mini-models are local regression models, which can be used for the function approximation learning. In the paper, there are presented mini-models based on hyper-spheres and hyper-ellipsoids and researches were made for linear and nonlinear models with no limitations for the problem input space dimension. Learning of the approximation function based on mini-models is very fast and it proved to h...

We have analyzed the far-field approximation of the Green’s function representation for seismic interferometry. By writing each of the Green’s functions involved in the correlation process as a superposition of a direct wave and a scattered wave, the Green’s function representation is rewritten as a superposition of four terms. When the scattered waves are modeled with the Born approximation, i...

This is a part of Part VI (nonlinear filtering) of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I [52] and Part II [48] deal with target motion models. Part III [49], Part IV [50], and Part V [51] cover measurement models, maneuver detection based techniques, and multip...

in this paper, the magnetohydrodynamic (mhd) boundary layer flow over a nonlinear porous stretching sheet is investigated by employing the homotopy perturbation transform method (hptm) and the pade´ approximation. the numerical solution of the governing non-linear problem is developed. comparison of the present solution is made with the existing solution and excellent agreement is noted. graphi...

Optimal control of systems with complex nonlinear behaviour such as steady state multiplicity results in a nonlinear optimization problem that needs to be solved online at each sample time. We present an approach based on simulation, function approximation and evolutionary improvement aimed towards simplifying online optimization. Closed loop data from a suboptimal control law, such as MPC base...

In this paper, a new approach is introduced to solve Blasius equation using parameter identification of a nonlinear function which is used as approximation function. Bees Algorithm (BA) is applied in order to find the adjustable parameters of approximation function regarding minimizing a fitness function including these parameters (i.e. adjustable parameters). These parameters are determined ho...

Stochastic Approach to Vehicle Routing Problem: Development and Theories Abstract In this article, a chance constrained (CCP) formulation of the Vehicle Routing Problem (VRP) is proposed. The reality is that once we convert some special form of probabilistic constraint into their equivalent deterministic form then a nonlinear constraint generates. Knowing that reliable computer software...

today, stock investment has become an important mean of national finance. apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. in the past, long-term trading processes and many technical analysis methods for stock market were put forward. however, stock market is a nonlinear sys...

This paper presents a novel approach for developing tracking controllers for nonlinear systems. The approach involves the numerical solution, by Galerkin approximation , of the time-varying Hamilton-Jacobi-Bellman (HJB) equation and results in a nonlinear controller approximating the optimal tracking control law for a spec-iied desired trajectory and cost function. Experimental results are pres...

In this article algorithms are developed for nonlinear n-term Courant element approximation of functions in Lp (0 < p ≤ ∞) on bounded polygonal domains in R2. Redundant collections of Courant elements, which are generated by multilevel nested triangulations allowing arbitrarily sharp angles, are investigated. Scalable algorithms are derived for nonlinear approximation which both capture the rat...