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

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.