## 1 Introduction
Active vibration isolation reduces the transmission of vibrations from the machine to the ground, and passive ones reduce the transmission of vibrations from the ground to the machine [5]. Machine learning is a field of science that focuses on creating methods and algorithms that allow for predicting relationships between variables or variables and results. This area has developed very intensively, especially in the last decade. Machine learning can have many potential applications and can be used in many scientific fields, from phase identification in structural steel [6] to an assessment of property value [7,8]; in speech and writing recognition software; in cybersecurity; and in banking, decision-making, and medicine. The number of potential applications for this technology is vast and increases every year. Under the general term machine learning, there are many different computational methods, one of the most popular being an artificial neural network (ANN) and the related deep neural network (DNN), which means an ANN with many hidden layers [9]. Moreover, machine learning can also be used to support vector machines, Bayesian networks, and genetic algorithms. In our study, we decided to utilize an ANN. An ANN is a powerful computing tool that can solve complex problems by imitating the behavior of the human brain. The primary computing unit of an ANN is an artificial neuron. One artificial neuron has a reduced ability to solve problems. However, many artificial neurons connected and put together in layers can give an excellent approximation of the problem under consideration. In order for an ANN to be practical, it should be fed with many examples [10]. Namely, we have to present several input parameters with predefinedor assigned weights, along with output solutions. During the learning process, we check whether the answer is consistent with the result assigned to it and whether it seeks the right result by changing the weights of individual parameters. The first ANN required the adjustment of parameter weights using potentiometers. Recently, the weighing process has been automated by introducing a learning rule. The architecture of an ANN consists of layers. We can distinguish three types of layers: the input layer and the output layer, which are associated with input parameters and output targets, and a hidden layer between them. The hidden layers perform computations on the input parameters and their weights that are later applied with activation functions to calculate a result. An ANN can be organized in many different ways. In the available literature, there are three categories in terms of which we can describe an ANN [10; 11]. It can be divided by learning paradigms, learning algorithms, and topologies, as in Figure 1.  
The first category refers to learning methods that we can divide into supervised and unsupervised learning and reinforced learning. In supervised learning, we strive for a defined result, while in unsupervised learning, we try to detect unknown relationships between variables. The second category, the learning algorithms, refers to how the data are processed to solve the problem. Data are transferred between layers differently, which affects both the calculation time and the quality of the obtained results. A basic ANN processes data in only one direction. A recursive ANN causes feedback loops, which calculate the results after reaching equilibrium [10; 11]. Within this category, we can mention the following learning algorithms: conjugate gradient, quasi-Newton, and steepest descent. The third category is a network topology. We have single-layer networks, multilayer networks, and networks with one recurrent layer. The more layers there are, the more powerful the network capability is at solving complex problems and finding nonlinear dependencies. The more neurons in the layer there are, the more input data the layer can handle [12; 13]. Figure 1: The organization of an artificial neural network (ANN) in machine learning.