Comparison of Fuzzy Functions for Low Quality Data GAP Algorithms
The undesired effects of data gathered from real world can be produced by the noise in the process, the bias of the sensors and the presence of hysteresis, among other uncertainty sources.
Data gathered by this way are called Low Quality Data (LQD). Thus, uncertainty representation tools are needed for using in learning models with this kind of data.
This work presents a method to represent the uncertainty and an approach for learning white box Equation Based Models (EBM). The proficiency of the representations with different noise levels and fitness functions typology is compared.
The numerical results show that the use of the described objectives improves the proficiency of the algorithms. It has been also proved that each meta-heuristic determines the typology of fitness function.
KeywordsLow Quality Data Simulated Annealing Genetic Programming Algorithm Equation Based Model
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