Data fusion in the decision-making process based on artificial neural networks

• Autorzy: Dudczyk Janusz , Rybak Łukasz , Jezierski Zdzisław

Identyfikatory

DOI

10.29119/1641-3466.2020.149.10

• Języki publikacji: EN

Abstrakty

EN

Purpose: The term data fusion is often used in various technologies, where a significant element is the ability of combining data of different typology coming from diverse sources. Currently, the issue of DF is developing towards interdisciplinary field and is connected with 'agile' data (information) synthesis concerning phenomena and objects. Optimal environment to carry out data fusion are SN (Sensor Networks), in which DF process is carried out on a data stage, most often automatically with the use of probable association algorithms of this data. The purpose of this article was an implementation of a neural network and its adaptation in the process of data fusion and solving the value prediction problem. Design/methodology/approach: The conducted experiment was concerned with modelling artificial neural network to form radiation beam of microstrip antenna. In the research the MATLAB environment was used. Findings: The conducted experiment shows that depending on the type of output data set and the task for ANN, the effect of neural network's learning is dependent on the activation function type. The described and implemented network for different activation functions learns effectively, predicts results as well as has the ability to generalize facts on the basis of the patterns learnt. Research limitations/implications: Without doubts, it is possible to improve the model of a network and provide better results than these presented in the paper through modifying the number of hidden layers, the number of neurons, learning step value or modifying the learning algorithm itself. Originality/value: The paper presents the implementation of the sensor network in the context of the process of data fusion and solution prediction. The paper should be read by persons which research interests are focused at the decision support by the information and communication technologies.

Słowa kluczowe

EN

data fusion; decision-making process; sensor networks; artificial neural network

PL

fuzja danych; proces decyzyjny; sieci sensorowe; sztuczna sieć neuronowa

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2020
• Tom: z. 149
• Strony: 97--108
• Opis fizyczny: Bibliogr. 8 poz.

Twórcy

autor

Dudczyk Janusz

• Institute of Computer Science and Technology, Stefan Batory State University

autor

Rybak Łukasz

• Institute of Computer Science and Technology, Stefan Batory State University

autor

Jezierski Zdzisław

• Institute of Security Sciences, Stefan Batory State University

Bibliografia

• 1. Anatriello, G. (2014). Iterated grand and small Lebesgue spaces. Collectanea Mathematica, 65(2), 273-284.
• 2. Cho, S.B., and Kim, J.H. (1995). Combining multiple neural networks by fuzzy integral for robust classification. IEEE Transactions on Systems, Man, and Cybernetics, 25(2), 380-384. doi.10.1109/21.364825.
• 3. Dudczyk, J., and Rybak, Ł. (2018). Adaptive Decision Support System in Network Centric Warfare Process. Elektronika: konstrukcje, technologie, zastosowania, nr 7, 9-42. doi: 10.15199/13.2018.7.10.
• 4. Hurvich. C., and Tsai, C.-L. (1990). Model selection for least absolute deviations regression in small samples. Statistics & Probability Letters, 9(3), 259-265.
• 5. Jolly, K.G., Ravindran, K.P., Vijayakumar, R., and Kumar, R.S. (2006). Intelligent decision making in multi-agent robot soccer system through compounded artificial neural networks. Robotics and Autonomous Systems, 55(7), 589-596. doi.org/10.1016/j.robot.2006.12.011.
• 6. Kahraman, C., Ruan, D., and Dogan, I. (2003). Fuzzy group decision-making for facility location selection. Information Sciences, 157, 135-153. Doi.org/10.1016/S0020-0255(03)00183-X.
• 7. Rybak, Ł., and Dudczyk, J. (2019). Increasing the information superiority on the modern battlefield through the use of virtual reality systems. Security and Defence Quarterly, 25(3).
• 8. Zhou, J., and Civco, D.L. (1996). Using Genetic Learning Neural Networks for Spatial Decision Making in GIS. Photogrammetric Engineering & Remote Sensing, 62(11), 1287-1295. Doi. 0099-1112/96/6211-1287$3.00/0.