Application of Relative Derivation Terms by Polynomial Neural Networks

A lot of problems involve unknown data relations, which can define a derivative based model of dependent variables generalization. Standard soft- computing methods (as artificial neural networks or fuzzy rules) apply usual absolute interval values of input variables. The new proposed differential polynomial neural network makes use of relative data, which can better describe the character regarding a wider range of input values. It constructs and resolves an unknown partial differential equation, using fractional polynomial sum derivative terms of relative data changes. This method might be applied to solve problems concerned a visual pattern generalization or complex system modeling.