Abstract
The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.
Highlights
The concept of communication of objects that use state-of-the-art technology to interact with one another and the environment [1] has drawn considerable attention both in the academic environment and in industry
The above-mentioned entails the method of increasing the accuracy of solving a regression problem based on a two-element General Regression Neural Network (GRNN) ensemble using the general concept of applying networks of this type [43]
The accuracy of the developed ensemble operation was compared with the outcomes of the state-of-the-art developments in the field of computational intelligence dealing with the problem of recovering missing data collected by Internet of Things devices
Summary
The concept of communication of objects that use state-of-the-art technology to interact with one another and the environment [1] has drawn considerable attention both in the academic environment and in industry. An IoT is a network of connected devices that can interact. The modern state of the industrial internet makes it possible to integrate a number of devices with different encoders into one entity [2]. A peculiar network is formed above the object of attention that all the devices are focused on. Within this network, there is a constant collection, processing, and exchange of information, based on which decisions are automatically made on the management of the object
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