Abstract
The purpose of this paper is to use the hybridized optimization method in order to find mathematical structures for analysis of experimental data. The heuristic optimization method will be hybridized with deterministic optimization method in order to that structures found require not knowledge about data generated experimentally. Five case studies are proposed and discussed to validate the results. The proposed method has viable solution for the analysis of experimental data and extrapolation, with mathematical expression reduced.
Highlights
This paper is an extended version of our paper published in 2016 IEEE 16th International Conference on Environment and Electrical Engineering [1]
Since fop(x) represents the regression and ε is the random additive error of the process that does not depend on "x" and satisfies the homoscedasticity criterion, which is, that the variance of ε is constant. In this sense it is said that fop(x) is the regression that represents the system if the mean square error (MSE) is as minimal as possible
In order to generate the set of experimental data, known and used functions have been used to evaluate regression processes in mathematics and statistics
Summary
This paper is an extended version of our paper published in 2016 IEEE 16th International Conference on Environment and Electrical Engineering [1]. Researches show the need to express the variable behavior through functions that represent experimental data. The absence of the function that describes the behavior of the system implies in simulations or experiments performing in order to define the outputs, every time the inputs are changed. Several times, this requires time and effort, which can make the process of study the system unpractical. The main contribution is to reduce the edge effect due to the reduced number of terms Besides that, it contributes to the recognition of systems from the experimental data and in assertive extrapolation at considerable intervals.
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