Authors S.I. Klevtsov
Month, Year 11, 2014 @en
Index UDC 629.3.066.3
Abstract To avoid and prevent abnormal situations, a model tracking state changes of a technical object in real time is developed. It is often enough to analyze the behavior of one the most important and relevant parameters, without regard to the other parameters that affect its state to the solution of this problem. Due to the fact that the tracking state change should take place in real-time microprocessor microcontroller unit, and preferably in the background pattern the requirements set to the algorithm and model on the ease of computation, and adaptation to characteristics of microprocessor data processing. Time for identification should be small, not significantly affect the performance of other tasks. For solving the problem of tracking changes in the state is used the model that is based on the Hamming neural network. The resulting modified Hamming neural network algorithm implements a simple operation, simple learning algorithm. Its capacity is not dependent on the dimension of the input signal. The resulting modified neural network model and tracking algorithm changes the state of the object function based on sequence analysis of the monitored parameters. The input network served binary input signals that can be effectively used in the implementation of the algorithm in the microcontroller.

Download PDF

Keywords Neural network; the object state change; real time.
References 1. Klevcov S.I. Prognozirovanie izmenenij fizicheskoj velichiny v real'nom vremeni s ispol'zovaniem linejnogo adaptivnogo fil'tra [Forecasting of changes in the physical quantities in real time using a linear adaptive filter], Izvestija JuFU. Tehnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2013, No. 5 (142), pp. 180-185.
2. Klevtsov S.I. Modelirovanie algoritma kratkosrochnogo prognozirovaniya izmeneniya bystroizmenyayushcheysya fizicheskoy velichiny v real'nom vremeni [The simulation algorithm of short-term forecasting changes rapidly changing physical quantities in real time], Inzhenernyy vestnik Dona [Engineering journal of Don], 2012, No. 3(21), pp. 199-205.
3. Klevtsov S.I. Osobennosti vybora parametrov nastroyki modeli sglazhivayushchego vremennogo ryada dlya osushchestvleniya kratkosrochnogo prognozirovaniya izmeneniya fizicheskoy velichiny [Choice of parameters for adjustment models of a smoothing time number for short-term forecasting of physical size], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2011, No. 5 (118), pp. 133-138.
4. Klevtsov S.I. Predvaritel'naya otsenka sostoyaniya sovokupnosti parametrov tekhnicheskogo ob"ekta s ispol'zovaniem intellektual'nogo mikroprotsessornogo modulya [The simplified estimation of the condition for set of parameters of technical object with use of the intellectual microprocessor module], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 5 (106), pp. 43-48.
5. Lipman R. An introdaction to computing with neural nets, IEEE Acoustic, Speech and Signal Processing Magazine, 1987, No. 2, pp. 4-22.
6. Golovko V.A. Neyronnye seti: obuchenie, organizatsiya i primenenie [Neural networks: training, organization and application]. Kn. 4. Moscow: IPRZhR, 2001, 256 p.
7. Nazarov A.V., Loskutov A.I. Neyrosetevye algoritmy prognozirovaniya i optimizatsii system [Neural network prediction algorithms and optimization systems]. St. Petersburg: Nauka i
tekhnika, 2003, 384 p.
8. Kruglov V.V. Borisov V.V. Iskusstvennye neyronnye seti. Teoriya i praktika [Artificial neural network. Theory and practice]. Moscow: Goryachaya liniya – Telekom, 2001, 382 p.
9. Wei Lu, Zhijian Li, Bingxue Shi. A modified Hamming neural network, Solid-State and Integrated Circuit Technology, 1995 4th International Conference. IEEE. Beijing, 1995, pp. 694-696.
10. Gupta A.K., Singh Y.P. Analysis of Hamming Network and MAXNET of Neural Network Method in the String Recognition, Communication Systems and Network Technologies (CSNT), 2011 International Conference. Katra, Jammu. 2011. IEEE, pp. 38-42.
11. Gaitanis N., Kapogianopoulos G., Karras D.A. Pattern classification using a generalised Hamming distance metric, Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993
International Joint Conference, 1993, Vol. 2, pp. 1293-1296.
12. Lamela Horacio, Ruiz-Llata M., Warde Cardinal. Prototype optoelectronic neural network for artificial vision systems, IECON 02. Industrial Electronics Society, IEEE 2002 28th Annual Conference, 2002, Vol. 2, pp 1434-1438.
13. Kwan H.K. One-layer feedforward neural network for fast maximum/minimum determination, Electronics Letters, 2002, Vol. 28, Issue 17, pp. 1583-1585.
14. Borisov E.S. Klassifikator na osnove neyronnoy seti Khemminga [The classifier on the basis of the Hamming neural network]. Available at:
15. Wei Lu, Bingxue Shi, Zhijian Li. A modified Hamming neural network with different thresholds and multi-valued weights, Neural Networks, 1996. IEEE International Conference, Vol. 2. Washington, DC, pp. 1012-1016.
16. Feng K., Hoberock L.L. An optimal scheduling of pick place operations of a robot-vision-tracking system by using back-propagation and Hamming networks, Robotics and Automation,
1992. Proceedings., 1992 IEEE International Conference, Vol. 2. Nice. IEEE, pp. 1201-1206.
17. Venkatalakshmi K., MercyShalinie S. Classification of multispectral images using neurostatistical classifier based on decision fusion and feature fusion, Intelligent Sensing and Information Processing, 2004. Proceedings of International Conference, 2004. IEEE, pp. 283-288.
18. Wei Lu, Bingxue Shi, Zhijian Li. A hybrid handwritten digits recognition system based on neural networks and fuzzy logic, Systems, Man, and Cybernetics, 1996, IEEE International Conference, Vol. 1. Beijing. IEEE, pp. 424-427.

Comments are closed.