Background:
Today, using of systems on the base of Internet of Things (ІоТ) devices is very widespread in
various applications. Intellectual analysis of the data collected by similar devices is an important task for efficient and
successful functioning of such systems. In particular, the reliability of such kind of analysis has greatly influence on the
ability to partially or fully automate certain processes or subsystems. However, imperfect devices of data collection,
transportation errors, etc. cause data missing to appear. A number of limitations cause this problem, and in the work, they
makes it impossible an effective intellectual analysis for specific use. That is why the scientific and applied problem of
effectively filling the missing in the data collected by the sensors of specific characteristics should be considered.
Methods:
The authors propose a new prediction method for solving this problem based on the use of General Regression
Neural Networks (GRNN).
Results:
The possibility of approximation and partial elimination of the error of computational intelligence of this type has
been analytically proved. A cascade of two sequentially connected GRNN was developed. The optimal parameters of the
developed cascade were selected. The simulation of its work was performed to solve the problem of recover missing sensor
data in the dataset for monitoring the state of air environment. A high number of missing for one reason or another
characterizes this real data set, collected by IoT device.
Conclusion:
High accuracy of cascade operation in comparison with existing methods of this class is inserted. All
advantages and disadvantages are described. Perspectives of further research are outlined.
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