The proposed estimator is based on the use of Affine Arithmetic (AA) and it is presented in two versions: (i) a general version suitable for differentiable nonlinear algorithms, and Linear Time-Invariant (LTI) algorithms with and without feedbacks; and (ii) an LTI optimized version.
We envision two uses of our divide-and-conquer local search heuristic: (1) as a stand-alone approach for data visualization, and (2) to provide a good approximate starting solution for a nonlinear algorithm.
A decision-based nonlinear algorithm for removal of strip lines, drop lines, blotches, band missing, and impulses in images is presented.
The main contribution of this paper is to enable us to propose nonlinear conjugate gradient algorithms for constrained smooth convex optimization which are examples of the proposed line search fixed point algorithms, in contrast to the previously reported results for nonlinear conjugate gradient algorithms for unconstrained smooth nonconvex optimization [20], Subchapter 5.2, [23 29].
The TS model is first trained offline using Gauss Newton Method for Nonlinear Regression (GNMNR) algorithm with data collected from both building and HVAC system equipments.
What is more, for nonlinear function, UKF algorithm can greatly improve the accuracy of the calculation in practical applications, if we apply the UKF algorithm to the method in [20], the issue would be a preferable basis for data fusion in the future and may have great significance for practical applications.
We call it the GKONP algorithm, shown as Fig. 4. It combines the FDP algorithm and a differential evolution (DE) algorithm for nonlinear programming.
The Levenberg Marquardt (LM) algorithm is basically a Hessian-based algorithm for nonlinear least squares optimization.
This paper addresses the convergence properties of implicit numerical solution algorithms for nonlinear hyperbolic transport problems.
García et al. [1] present a class of column generation (CG) algorithms for nonlinear programs.
