Genetic algorithm optimal control of gravity seed cleaner by radial baisis function neural network modeling

For solving the problems that seed cleaner control system cannot classify seeds varying with different types, varieties and batch, a method is presented for controlling nonlinear static systems with an example of gravity seed cleaner. In this controlling scheme, nonlinear static system is modeled by using Radial Basis Function(RBF) neural network, and then genetic algorithm uses the model to optimize the control system, meanwhile, the actual system got the data of input and output which were used to train the RBF neural network repeatedly for better mapping to nonlinear system. The proposed approach is applied to the gravity seed cleaner control system. In comparison with the results of manually adjusted classification, the total seed selection percentage by GA optimal control was raised. This genetic algorithm provides some references for the same kind of control system.