DEVELOPMENT OF A STRUCTURE AND ALGORITHM BASED ON NEURAL NETWORKS FOR THE DETECTION AND ANALYSIS OF UNDERGROUND DEPOSITS

In the last decade, artificial neural networks and machine learning have become the most used areas for solving complex real-world problems. In particular, problems that are considered too difficult or in some cases impossible to solve with computers are of increasing interest in both academia and industry. This paper considers the digital processing of one- and two-dimensional signals using an artificial neural network. Neural networks are based on the so-called activating function of neurons. This feature is critical to network performance but is often neglected . In most cases, one of the non-adaptive functions is chosen as the activation function. Adaptive sigmoid or ReLU functions are used in many cases, but these functions have disadvantages because adapting data from one limited area affects the overall results. Therefore, the paper proposes the use of flexible quadratic B-spline functions with free nodes. Spline is an activation function that expands the scope and exactly satisfies the approximation condition. This prevents overfitting in neural networks. Including the recurrence property of splines corresponds to the structure of recurrent neural networks.