Multilayer neural networks – as determined systems

: pp.26-31
Ivan Franko National University of Lviv
Institute of Technical Engineering the State Higher School of Technology and Economics in Jarosław
Ivan Franko National University of Lviv
Ivan Franko National University of Lviv
Ivan Franko National University of Lviv
Ukrainian Academy of Printing
Ivan Franko National University of Lviv
Ivan Franko National University of Lviv

The study of the influence of learning speed (η) on the learning process of a multilayer neural network is carried out. The program for a multilayer neural network was written in Python. The learning speed is considered as a constant value and its optimal value at which the best learning is achieved is determined. To analyze the impact of learning speed, a logistic function, which describes the learning process, is used. It is shown that the learning error function is characterized by bifurcation processes that lead to a chaotic state at η> 0.8. The optimal value of the learning speed is determined. The value determines the appearance of the process of doubling the number of local minima, and is η = 0.62 for a three-layer neural network with 4 neurons in each layer. Increasing the number of hidden layers (3 ÷ 30) and the number of neurons in each layer (4 ÷ 150) does not lead to a radical change in the diagram of the logistic function (xn, η), and hence, in the optimal value of the learning speed.

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