Variable structure objects remodelling based on neural networks

The paper proposes the approach for uniform presentation of technical objects and technological processes with a variable structure. This approach is based on the approximation of specific models describing the object or the process by a uniformed remodeling class. The application of this approach is useful while solving optimization and control problems. Neural network models, which proved their high approximating capability, are suggested as a remodelling class. Application of the given approach is considered by the example of inertial torque transformer (ITT) workflow modelling. This process has a cyclical pattern where each phase of the cycle is divided into four segments described by various systems of nonlinear differential equations with the same parameters. Moreover, the solution to the system describing the next segment depends on the solution to the system obtained by the previous segment. It noticeably makes it difficult to determine the optimum ITT parameters as the fit function has the solution to the system of equations describing the last, the fourth segment of the cycle. The neural network model allows simplifying the solution to the given problem for each cycle segment.