Classification of biomolecules by information entropy

Algorithms for classification and taxonomy based on criteria such as information entropy and its production are proposed. In the first example, the feasibility of replacing a given anaesthetic by similar ones in the composition of a complex drug is studied. Some local anaesthetics currently in use are classified using characteristic chemical properties of different portions of their molecules. Another example refers to the detailed comparison of the sequences (primary structures) of biomolecules, proteins or nucleic acids which allows the reconstruction of a molecular phylogenetic tree for some vertebrates

Modified hildreth’s method applied in multivariable model predictive control

A significantly important part of model predictive control (MPC) with constraints are algorithms of numerical optimization. Reduction of the computational complexity of the optimization methods has been widely researched. The reason is that in certain cases of predictive control of fast dynamics processes an optimization algorithm may not be feasible within the sampling period time. This situation occurs particularly when requirements on control are more complex, e.g. in the multivariable control. Hildreth’s method based on the dual-problem-optimization-principles has been widely applied and implemented in model predictive control. However, modifications of this method are not widely described in context of model predictive control. This paper proposes a modification of Hildreth’s method, which reduces the computational complexity of the algorithm, and its application in the multivariable predictive control. © 2019, Springer International Publishing AG, part of Springer Nature

Applied quadratic programming with principles of statistical paired tests

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. © 2019, Springer Nature Switzerland AG