Artificial neural networks are computing systems that work much like the neurons in the human brain. Neural networks have proven to be an excellent tool for identifying hidden patterns and correlations in data. Because of that, they are used to solve various complex problems. The master's thesis analyzes B-splines artificial neural networks. The initial chapters present polynomial interpolation and piecewise polynomial functions. In the following, we learn about artificial neural networks, their functions and problems that can occur in the process of training artificial neural networks. This is followed by the implementation of artificial neural networks and the implementation of B-splines in artificial neural networks.
In the master's thesis neural networks are used for predicting Microsoft stock price based on previous stock prices. Since stock price, in general, depends on many factors, it is difficult to achieve accurate forecast. The thesis aims to investigate whether artificial neural networks and neural networks with B-splines are suitable for predicting time series and whether B-splines improve the performance of artificial neural networks. In the master's thesis, B-splines in neural networks are implemented as activation functions. Specifically, for the activation function, we used a spline that is formed from B-splines. Also, regular neural networks with sigmoid, hyperbolic tangent or ReLU activation function are implemented, with which we compare the obtained results.
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