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In this paper, a possibility of discovering laws governing empirical data whose interrelations can be expressed in a multidimensional polynomial form is considered. A novel atypical perceptron with reciprocal type activation functions is proposed. This perceptron implements the polynomial relation and enables determining the polynomial coefficients by training the perceptron. The perceptron is simple...
In this paper, a possibility of discovering laws governing empirical data by means of special type neural networks is discussed. We outline main idea and present new networks suitable for this task. The network presentation is combined with a preliminary classification of the applied symbolic relationships used to describe a given numerical data. We also show what operators can play a role of activation...
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