Fresh approaches to the construction of parameterized neural network solutions of a stiff differential equation

The additional conditions in equation form derived from the asymptotic decomposition were used apart from the point data. The classical and non-classical definitions of the problem were compared by entering a parameter into the complementary data. A new sampling scheme of test point choice at different stages of minimization (the procedure of test point regeneration) under various initial conditions was investigated. A way of combining two approaches (classical and neural network) based on the Adams PECE method was considered.