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Applied Quadratic Programming with Principles of Statistical Paired Tests

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1047)

Abstract

In applied research areas, various types of mathematical disciplines have been advantageously connected together with wide corresponding applications. As an applied proposal of this connection, a numerical optimization method of the quadratic programming particularly modified by a principle of statistical hypothesis testing can be seen in this paper. With regards to a computational complexity, algorithms of multivariable Model Predictive Control (MPC) can be considered as procedures with a higher computational complexity caused by the multi-variability, higher horizons and included constraints conditions. A wide spectrum of modifications has been proposed in the optimization subsystem of MPC controller yet; however, approaches based on including the hypotheses testing have not been widely considered in applied optimization method. A number of operations should be decreased; however, a control quality may be slightly influenced with regards to this aim. Therefore, the proposed modification is advantageous in an applied form of the quadratic programming technique where necessary information for following steps of a process control are provided. Achieved results are discussed in order to the incorporating of the principle of hypotheses testing in the modified numerical method of the applied quadratic programming.

Keywords

Hypothesis testing Optimization Quadratic programming Multivariable MPC Control quality 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mathematics with Didactics, Faculty of EducationUniversity of OstravaOstravaCzech Republic
  2. 2.Department of Process Control, Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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