INFORMATION-EXTREME MACHINE TRAINING SYSTEM OF FUNCTIONAL DIAGNOSIS SYSTEM WITH HIERARCHICAL DATA STRUCTURE
Keywords:information-extreme machine learning, categorical functional model, information criterion, control tolerance system, functional diagnostics, laser printer
Context. The problem of information-extreme machine learning of the functional diagnosis system is considered by the example of recognizing the technical state of a laser printer by typical defects of the printed material. The object of the research is the process of hierarchical machine learning of the functional diagnosis system of an electromechanical device.
Objective. The main objective is to improve the functional efficiency of machine learning during functional diagnostics system retraining using automatically forming a new hierarchical data structure for an expanded alphabet of recognition classes.
Method. A method of information-extreme hierarchical machine learning of the system of functional diagnosis of a laser printer based on typical defects of the printed material is proposed. The method was developed with functional approach of modeling the cognitive processes of natural intelligence, which makes it possible to give the diagnostic system the properties of adaptability under arbitrary initial conditions for the formation of images of printing defects and flexibility during retraining of the system due to an increase in the power of the alphabet of recognition classes. The method is based on the principle of maximizing the amount of information in the process of machine learning. The process of information-extreme machine learning is considered as an iterative procedure for optimizing the parameters of the functioning of the functional diagnostics system according to the information criterion. As a criterion for optimizing machine learning parameters, a modified Kullback’s information measure is considered, which is a functional of the exact characteristics of classification solutions. According to the proposed categorical functional model, an information-extreme machine learning algorithm has been developed based on a hierarchical data structure in the form of a binary decomposition tree. The use of such a data structure makes it possible to split a large number of recognition classes into pairs of nearest neighbors, for which the optimization of machine learning parameters is carried out according to a linear algorithm of the required depth.
Results. Information, algorithmic software for the system of functional diagnostics of a laser printer based on images of typical defects in printed material has been developed. The influence of machine learning parameters on the functional efficiency of the system of functional diagnostics of a laser printer based on images of defects in printed material has been investigated.
Conclusions. The results of physical modeling have confirmed the efficiency of the proposed method of information-extreme machine learning of the system of functional diagnosis of a laser printer based on typical defects in printed material and can be recommended for practical use. The prospect of increasing the functional efficiency of information-extremal learning of the functional diagnostics system is to increase the depth of machine learning by optimizing additional parameters of the system’s functions, including the parameters of the formation of the input training matrix.
Rodgers S. Laser Printer: The Definitive Guide to Laser Printer Wireless, Laser Printer Paper and More. NY, Lulu Press Inc, 2015, 70 p.
Lhotka B. P. Hacking the Digital Print. USA, New Riders, 2015, 310 p.
Sun W., Yao B., Zeng N. et al. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network, 2017, Vol. 10, No. 7, P. 790.
Henao H., Capolino G. A., et al. Trends in fault diagnosis for electrical machines: A review of diagnostic techniques, IEEE Industrial Electronics Magazine, 2014, Vol. 8, No. 2. pp. 31–42.
Engelbrecht A. Computational intelligence: an introduction. Sidney, John Wiley & Sons, 2007, 597 p. DOI: 10.1002/9780470512517
Reinartz T. A unifying view on instance selection, Data Mining and Knowledge Discovery, 2002, No. 6, pp. 191–210. DOI: 10.1023/A:1014047731786
Xu G., Zong Y., Yang Y. Z. Applied Data Mining. CRC Press, 2013, 284 p.
Dua Sumeet, Xian Du Data Mining and Machine Learning in Cybersecurity, 1st Edition. Auerbach Publications, 2011, 256 p
Aha D. W., Kibler D., Albert M. K. Instance-based learning algorithms, Machine Learning, 1991, No. 6, pp. 37–66. DOI: 10.1023/A:1022689900470
Petr Dolezel, Pavel Skrabanek, Lumir Gago Pattern recognition neural network as a tool for pest birds detection, Computational Intelligence (SSCIIEEE Symposium Series on), 2016, pp. 1–6.
Huang Ching-Lien, Hsu Tsung-Shin, Liu Chih-Ming The Mahalanobis-Taguchi system – Neural network algorithm for data-mining in dynamic environments, Expert Systems with Applications: An International Journal, 2009, Vol. 36, Issue 3, pp. 5475–5480.
Ammour H., Alhichri A., Bazi Y. et al. Deep Learning Approach for Car Detection in UAV Imagery, Remote Sens, 2017, Vol. 9, No. 4, pp. 1–15.
Dong S., Xu X., Liu J. et al. Rotating Machine Fault Diagnosis Based on Locality Preserving Projection and Back Propagation Neural Network-Support Vector Machine Model, Measurement and Control, 2015, Vol. 48, No. 7, pp. 211–216.
Gerrit J. J., Burg van den, Groenen P. J. GenSVM: A Generalized Multiclass Support Vector Machine, Journal of Machine Learning Research, 2016, Vol. 17, No. 224, pp. 1–42.
Shi Y., Mizumoto M. An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules, Fuzzy sets and systems, 2001, Vol. 118, No. 2, pp. 339–350.
Subbotin S. A. The neuro-fuzzy network synthesis and simplification on precedents in problems of diagnosis and pattern recognition, Optical Memory and Neural Networks (Information Optics), 2013, Vol. 22, No. 2, pp. 97–103. DOI: 10.3103/s1060992x13020082.
Efendigil T. Önüt S., Kahraman C. A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: a comparative analysis, Expert Systems with Applications, 2009, Vol. 36, No. 3, pp. 6697–6707.
Moskalenko V. V., Korobov A. G. Information-extreme algorithm of the system for recognition of objects on the terrain with optimization parameter feature extraction, Radio Electronics, Computer Science, Control, 2017, No. 2, pp. 38–45.
Moskalenko V. V., Dovbysh A. S., Naumenko I. V. et al. Improving the effectiveness of training the on-board object detection system for a compact unmanned aerial vehicle, Eastern-European Journal of Enterprise Technologies, 2018, Vol. 4/9, No. 94, pp. 19–26.
Protsenko O., Savchenko T., Myronenko M. et al. Informational and extreme machine learning for onboard recognition system of ground objects, Dependable Systems, Services and Technologies, XI International Conference, Kyiv, 14–18 May 2020, proceedings. Kyiv, IEEE, 2020, pp. 213–218.
Dovbysh A. S., Budnyk M. M., Piatachenko V. Yu et al. Information-Extreme Machine Learning of On-Board Vehicle Recognition System, Cybernetics and Systems Analysis, 2020, Vol. 56(4), pp. 534–543.
Simonovskiy J., Piatachenko V., Myronenko N. On-board Geographic Information System of Images’ Identification, Advanced Information Systems and Technologies : VI International Conference, Sumy, 16–18 May 2018, proceedings. Sumy, Sumy State University, 2018, pp. 115 – 118.
Dovbysh A., Zimovets V. Hierarchical Algorithm of the Machine Learning for the System of Functional Diagnostics of the Electric Drive, Advanced Information Systems and Technologies, VI International Conference, Sumy, 16–18 May 2018: proceedings. Sumy, Sumy State University, 2018, pp. 85–88.
Zimovets V. I., Shamatrin S. V., Olada D. E et al. Functional diagnostic system for multichannel mine lifting machine working in factor cluster analysis mode, Journal of Engineering Sciences, 2020, Vol. 7(1), pp. E20–E27. DOI: 10.21272/jes.2020.7(1).e4.
Dovbysh A., Piatachenko V. Hierarchical Clustering Approach for Information-Extreme Machine Learning of Hand Brush Prosthesis, Computational Linguistics and Intelligent Systems, V International Conference, Lviv, 22–23 April 2021, proceedings. Lviv, CEUR-WS, 2021, pp. 1706–1715.
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