Abstract
The paper presents algorithms for processing the measurement signal with the possibilities of adaptation, learning and decision making. A comparative analysis of the methods of intellectual processing of measurement data is carried out. A model of a measuring instrument for determining the structure of a neural network has been developed. The problem of error reduction due to measurement noise filtering with the use of neural networks is considered. The structure of the neural network has been developed for intelligent processing of the measurement signal and ensuring the implementation of the functions of reconfiguration, calibration, self-diagnosis and self-control. A neural network training algorithm based on error back propagation was used. The results of the implementation of the neural network algorithm in measuring instruments with different training patterns are presented. The paper also describes the calibration of linear and non-linear smart sensors. The results of the study show that the proposed algorithm improves the quality of measurement of technological parameters.
First Page
15
Last Page
21
References
- David, A., Dornfeld, А, DeVries, M.F. (2018). Neural Network Sensor Fusion for Tool Condition Monitoring. Sensor. 39(1), 101-105.
- Jinjiang, W., Junyao, X., Rui Zhao, Laibin Zhang (2017). Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics and Computer-Integrated Manufacturing, 45, 47-58.
- Ferreira, P., Ruano, A., Silva, S., Conceição, E. (2012). Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy Build. 55, 238-251.
- Juan, P., Francisco, Z., Paloma, B. (2015). Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes. Sensors, 15 (4), 9278-9304.
- Alvarez, J., Redondo, J., Camponogara, E. (2012). Optimizing building comfort temperature regulation via model predictive control. Energy Build. 57, 361-372.
- Schatten, R., Eckmiller, R. (2010). Enhancing active vision by a neural movement predictor. Proc. Int. Joint Conf. on Neural Networks, 2, 1312-1317.
- Meigal, A.Y., Gerasimova-Meigal, L.I., Reginya, S.A., Soloviev, A.V., Moschevikin, A.P. (2022). Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of “Dry” Immersion. Sensors, 22, 7915-7919.
- Ho, N.S.K., Tong, K.Y., Hu, X.L., Fung, K.L., Wei, X.J., Rong, W., Susanto, E.A. (2011). An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: Task training system for stroke rehabilitation. In Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics, 1-5.
- Hooshmand, R.A., Joorabian, M. (2006). Design and optimisation of electromagnetic flowmeter for conductive liquids and its calibration based on neural networks. IEE Proceedings Science Measurements and Technologies. 153, 139-146.
- Loskutov, A.I., Vecherkin, V.B., Shestopalova, O.L. (2012). Automation of state control of complex technical systems based on the use of a finite automaton model and neural network structures. Information and control systems, 2(57), 74-81.
- Buyankin, V.M. (2006). Application of an artificial neural network in the mode of identification of the dynamic parameters of an electric motor. Moscow State Technical University. N. E. Bauman. Series: Instrumentation, 3(64), 25-30.
Recommended Citation
Yusupbekov, N.R; Avazov, Y.Sh.; and Ruziev, Umidjon PhD
(2023)
"USE OF NEURAL NETWORKS IN INTELLIGENT MEASUREMENT TOOLS,"
Chemical Technology, Control and Management: Vol. 2023:
Iss.
4, Article 3.
DOI: https://doi.org/10.59048/2181-1105.1502
Included in
Complex Fluids Commons, Controls and Control Theory Commons, Industrial Technology Commons, Process Control and Systems Commons