Design On Steady-state Optimization Of Industrial Process Based On NN-GA

With the improvement of the industrial automation, steady control of the industrial processes has realized already. However, because of some slow disturbances and so on during the operation of the complicated industry systems, most systems will departure from their optimal operation states. We must make a steady-state optimization to look for the best operational parameters and adjust these parameters in time. In this way, the systems can work on the optimal operation states and the urgent requirements of the people can be fulfilled that to increase economic efficiency and to reduce energy consumption. Therefore, the steady-state optimization of the industrial process becomes the focal point to be studied.In this paper, the development, current situation and the significance of the industrial steady-state optimization are introduced and the theory of the neural network (BP network mainly) and the genetic algorithm are studied. In the industrial process there are strong non-linearity and complicated mechanism, it is difficult to establish an accurate model of the connection of the production goals with the operational parameters. In allusion to this problem, BP neural network is chosen to make a model and an improved BP algorithm is used for training the network. The improved BP algorithm — LMBP algorithm has reduced the calculation, improved the convergence rate and made the performance function value of the network reduce constantly to meet requirements for precision of modeling by adjusting the values of parameters.As to the optimal algorithm, the genetic algorithm is adopted. It can carry on the operation on a population composed by an encoding collection of variables and realize an effective search and the optimal solution of overall population. Nevertheless, in its application there is an earlier convergence. In this paper,...