Czasopismo | ELEKTRONIKA - KONSTRUKCJE, TECHNOLOGIE, ZASTOSOWANIA | Rocznik 2015 - zeszyt 4

Evolution method aided by knowledge base to improve learning effects of some neural networks

10.15199/13.2015.4.5

nr katalogowy: 90825
10.15199/13.2015.4.5

Creating mathematical models of unknown rules governing empirical data is an important but not easy task. One of ways to carry out this is to apply an approximation technique. To popular approximation functions belong polynomials. In order to obtain a polynomial-based approximation one has to determine values of the polynomial coefficients. This can be done in a variety of ways. Both deterministic and probabilistic methods are applied. One of them is using neural networks, where the coefficients are obtained in the way of learning the network. In this paper, instead of single polynomial rational functions (ratio of two polynomials) are proposed to be the main approximation description. This means that subject of the network learning are coefficients of the rational functions. Superiority of the rational-function-based special networks over the networks with a single polynomial is that the former is more flexible and better suited for the training task. If needed, one can also obtain a polynomial description making use of the rational function. To do this, proper conversion of the rational function must be done, which allows us to retain simplicity of network structure. Even though networks with rational functions may seem to by more complex than the ones using single polynomials, in reality the opposite situation takes place when using the proposed networks. An example illustrating this issue is presented in the paper. The proposed networks have such characteristic features as: 1) the use of reciprocal activation functions of the 1/(.) type as activation functions included in the network; 2) different from typical location of the activation functions, i.e. predominantly in the hidden layer and input nodes. As already mentioned, learning the special network is a way to determine coefficients of the used approximation function [1-13]. Main problems concern the learning and appear if the considered experimental data rules are p[...]

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